Candidate Cluster Head Research Articles

Partitioned uneven cluster routing algorithm based on gray wolf optimization in WSNs

WSNs have various uses across numerous industries and are one of the essential technologies of modern life. Energy consumption is the issue that has drawn the greatest attention and still has to be resolved because the nodes that comprise WSNs have a limited amount of energy. Numerous factors influence energy consumption, and our algorithm design considerations are centered on extending the network lifetime and energy efficiency through the resolution of imbalance and hotspot issues related to WSNs clustering. Because of this, we suggest a partitioned uneven cluster routing algorithm based on gray wolf optimization. To find the ideal cluster head, we first divide the network into areas with distinct important influence factors, then we improve the final cluster head election function and the candidate cluster head competition radius. Subsequently, to reduce the energy consumption resulting from multiple rounds of clustering, similarity determination is introduced. Finally, the optimal transmission path in the multi-hop process is obtained by combining the Gray Wolf optimization algorithm with the relay node selection function. Simulation results show that the network lifetime of the proposed algorithm is extended by 54.6 %, 46.2 %, 58.6 %, and 18.5 % compared to LEACH, DEBUC, LEACH-EDP, and LEACH-IM, respectively. The energy efficiency of the proposed algorithm is extended by 40.8 %, 7.1 %, 22.7 %, and 34.0 %, respectively. The proposed algorithm significantly extends the network lifetime and improves the energy efficiency of the network.

D2D Matching Techniques for Resource Allocation Management and Power Control Management in B5G/6G

The upcoming 6G network is expected to have a great integration of Device-to-Device (D2D) technology. One of the key advantages of D2D technology is its potential to minimize the load on cellular base stations, which can help extend the lifespan of cellular network infrastructure. D2D multicast communication can meet the growing demand for multimedia content in local area services by maximizing energy efficiency and device lifespan by reusing the same resources for the cellular network. In this paper, we investigate an efficient resource allocation scheme by using the One-To-Many Gale Shapley pairing algorithm (GSA) for efficient allocation and power optimization. We propose a joint optimization approach for D2MD clusters that considers the Signal-to-Interference Noise ratio and energy levels of devices' batteries. The problem is optimized, so it is divided into two convex sub-problems. In the first sub-problem, power allocation is performed for each candidate cluster-head (CH)and cellular user to maximize energy efficiency using the Dinkelbach matching algorithm. In the second sub-problem, the One-to-Many Gale Shapley matching algorithm is used to optimize resource allocation and cluster-head selection to select the Cluster Nodes (CRns) to form the cluster. Numerous investigations show that the suggested technique maintains QoS and minimal battery power requirements while increasing cluster-head longevity and energy efficiency (EE) in D2D applications.

Improved beluga whale optimization algorithm based cluster routing in wireless sensor networks.

Cluster routing is a critical routing approach in wireless sensor networks (WSNs). However, the uneven distribution of selected cluster head nodes and impractical data transmission paths can result in uneven depletion of network energy. For this purpose, we introduce a new routing strategy for clustered wireless sensor networks that utilizes an improved beluga whale optimization algorithm, called tCBWO-DPR. In the selection process of cluster heads, we introduce a new excitation function to evaluate and select more suitable candidate cluster heads by establishing the correlation between the energy of node and the positional relationship of nodes. In addition, the beluga whale optimization (BWO) algorithm has been improved by incorporating the cosine factor and t-distribution to enhance its local and global search capabilities, as well as to improve its convergence speed and ability. For the data transmission path, we use Prim's algorithm to construct a spanning tree and introduce DPR for determining the optimal route between cluster heads based on the correlation distances of cluster heads. This effectively shortens the data transmission path and enhances network stability. Simulation results show that the improved beluga whale optimization based algorithm can effectively improve the survival cycle and reduce the average energy consumption of the network.

Energy-Efficient Improved Optimal K-Means: Dynamic Cluster Head Selection based on Delaying the First Node Death in MWSN-IoT

The Internet of Things (IoT), which attaches dynamic devices that can access the Internet to create a smart environment, is a tempting research area. IoT-based mobile wireless sensor networks (WSN-IoT) are one of the major databases from which the IoT collects data for analysis and interpretation. However, one of the critical constraints is network lifetime. Routing-based clustered protocols and cluster head (CH) selection are crucial in load balancing and sensor longevity. Yet, with clustering, sensor node mobility requires more overhead because the nodes close to the center may get far and thus become unsuitable to be a CH, whereas those far from the center may get close and become good CH candidates, influencing energy consumption. This paper suggests an energy-efficient clustering protocol with a dynamic cluster head selection considering the distance to the cluster center, remaining energy, and each node's mobility degree implementing a rotation mechanism that allows cluster members to be equally elected while prioritizing those with the minimum weight. The advised algorithm aims to delay the first node death (FND) and thus prolong the stability period and minimize energy consumption by avoiding re-clustering. The performance of the proposed protocol exceeds that achieved in our previous work, Improved OKmeans, by 11%, in first dead node lifetime maximization, 43% in throughput, and 44% in energy efficiency.

WSN Clustering Routing Algorithm Combining Sine Cosine Algorithm and Lévy Mutation

Directed against the disadvantages of relatively short life-cycle and unbalanced energy utilization among nodes in WSN, a clustering routing algorithm combining sine cosine algorithm and Lévy mutation is developed. During the cluster head election stage, the amount of cluster heads is dynamically calculated according to the surviving nodes for keeping it at a reasonable value; taking full account of the current energy of nodes, only nodes with high energy can be candidate cluster heads; the fitness function is constructed according to inter-cluster distance, so that the distribution structure within the cluster are relatively uniform; the Sine Cosine Algorithm with improved step size search factor is used for cluster head election, and Lévy mutation is introduced to realize the variation of population. The group of individuals with the lowest fitness function value is used as final election scheme for current round. In the data transmission phase, for the sake of avoiding long-distance transmission, the relay node is designed to forward data. The proposed algorithm effectively extends network life-cycle and well equilibriums the load of network nodes.

Ethernet Information Security Protocols Based on Industrial Control Wireless Sensor Networks

This paper provides an in‐depth study and analysis of information security protocols for industrial Ethernet using wireless sensor networks. The optimal number of cluster heads for nonuniform subclustering is derived based on the sensor energy consumption model, and then, the EEUC contention radius formula is optimized to select candidate cluster heads with random values and energy as weights. A multihop approach based on the shortest offset is also proposed for intercluster information transmission. Experimental results show that the EEUC‐based improved cluster routing protocol proposed in this paper balances the node energy consumption and extends the network lifetime. In response to the problem that the coarsened hop value and average hop distance of the DV‐Hop localization algorithm cannot reflect the network topology, the improved DV‐Hop algorithm based on multicommunication radius and hop distance correction is proposed. Simulation experiments show that the improved algorithm based on multiple communication radius and hop count correction can significantly reduce the localization error and improve the accuracy of the algorithm. Aiming at the shortcomings of MRP with excessive risk concentration and transmission medium limitation, this paper proposes a fast self‐healing mechanism of industrial Ethernet with a multiexpert strategy. The PCP‐AP common platform architecture for openSAFETY sites is designed on the base sleeve of the implementation of the industrial Ethernet protocol Ethernet POWERLINK; the main communication part of POWERLINK is implemented through an FPGA hardware solution, and the openSAFETY site is implemented using AM335X high‐performance processor to implement openSAFETY security application functions. Finally, the article conducts field tests on the wireless signal information transmission, WSN data transmission, network connection, and power supply system in the system and compares and analyzes the data collected by the system with the monitoring data of the national control site. The data obtained by the system has real reliability. The communication module used is inexpensive, lightweight, and easy to operate. It can realize the collection of multiple pollution sources, and compared with traditional monitoring equipment, it avoids the difficulties of complicated wiring, difficult positioning of pollution sources, and restricted monitoring areas and largely reduces the investment in human and material resources.

Secure Clustering Strategy Based on Improved Particle Swarm Optimization Algorithm in Internet of Things.

This paper proposes a secure clustering strategy based on improved particle swarm optimization (PSO) in the environment of the Internet of Things (IoT). First, in the process of cluster head election, by considering the residual energy and load balance of nodes, a new fitness function is established to evaluate and select better candidate cluster head nodes. Second, the optimized adaptive learning factor is used to adjust the location update speed of candidate cluster head nodes, expand the local search, and accelerate the convergence speed of global search. Finally, in the stage of forwarding node election and data transmission, in order to reduce the energy consumption of forwarding nodes, each cluster head node elects a forwarding node among the ordinary nodes in its cluster, so that the elected forwarding nodes have the optimal energy and location relationship. Experiments show that the proposed method effectively prolongs the network lifetime compared with the comparison methods. The average node degree of the proposed method is less than 2.5.

Socially-aware and energy-efficient resource allocation and power control for D2D multicast content distribution

Nowadays, a significant portion of the base station's traffic is dedicated to the increasing demand for the same multimedia content in local area services. Device-to-device (D2D) multicast communications are considered an effective and practical approach for tackling such a challenge in 4G/5G cellular networks. Due to the limited power source of the devices, this paper aims to jointly maximize their energy efficiency and lifetime while satisfying the quality of service (QoS) and the minimum battery power of users. To accomplish this aim, D2D multicast clusters are first formed considering the information of the physical distance, the social distance, and the energy levels of the devices' batteries. Then, cluster-head selection, power control, and resource allocation are carried out to maximize the product of energy efficiency and lifetime, which converts the multi-objective problem to a single-objective one. This optimization problem is non-convex and nonlinear; thus, it is divided into two convex sub-problems. In the first sub-problem, for each candidate cluster-head and corresponding cellular user, the power allocation is performed to maximize energy efficiency. Then, the resource allocation and the cluster-head selection are performed by solving the second sub-problem using the one-to-one Hungarian matching algorithm to maximize the life-energy efficiency. Results of extensive evaluation show that our proposed scheme improves energy efficiency and increases the cluster-head life in D2D multicast services.

A K-Means Clustered Routing Algorithm with Location and Energy Awareness for Underwater Wireless Sensor Networks

Data delivery in harsh underwater channels consumes a higher transmission power than that in terrestrial networks. However, due to the complexity of the underwater environment, the energy supply of the nodes in underwater wireless sensor networks is usually limited by their required laborious battery replacement. Thus, energy consumption is considered one of the key issues in underwater wireless optical communication. To minimize such consumption for underwater transmission nodes, much research interest has been found on K-Means technology in designing routing algorithms. However, these algorithms have not regarded the located site and the remaining energy of the underwater nodes simultaneously, which might affect their efficiency. In this paper, we propose a clustered routing algorithm, namely the location and energy-aware k-means clustered routing (LE-KCR) algorithm, which applies K-means technology regarding both the located site and the remaining energy of each node. In the proposed LE-KCR algorithm, both the located site and the remaining energy of a candidate cluster-head, as well as the distance between it and its sink node, are considered in cluster-head selection. In addition, given the inaccessibility of some nodes to the whole underwater sensor network resulting from the limited transmission range of their clusters, the dual-hop routing technique is adopted for the edge nodes. The simulation results indicate that the proposed LE-KCR algorithm remarkably reduces the energy consumption and the dead nodes when compared to the traditional low-energy adaptive clustering hierarchy (LEACH) protocol and the optimized LEACH protocol based on K-means clustering technology.

A Distributed Unequal Clustering Routing Protocol Based on the Improved Sine Cosine Algorithm for WSN

In order to balance the overall energy consumption and improve the energy efficiency of wireless sensor network (WSN), a distributed energy-balanced unequal clustering routing protocol based on the improved sine cosine algorithm (DUCISCA) is proposed. Firstly, DUCISCA adopts a time-based cluster head competition algorithm. In this algorithm, the broadcast time depends on the residual energy of the candidate cluster head, the distance to the base station, and the number of neighbour nodes. Secondly, a competition radius considering the distance from node to base station and the residual energy of node is proposed. It can balance energy consumption of nodes in different locations to avoid the “hot spot” problem. At the same time, it adopts a time-based broadcast mechanism. The waiting time depends on the residual energy of CCHs, the distance to the BS, and the number of neighbour nodes, which can effectively reduce the overhead of nodes. Thirdly, the energy of cluster head, the number of neighbour nodes, and the distance from the ordinary node to the cluster heads need to be taken into account to get a better clustering result. Finally, in order to speed up convergence and improve the ability to jump out of local optimum, the improved sine-cosine algorithm (ISCA) based on Latin hypercube sampling and adaptive mutation is proposed. The improvement strategies adopted by ISCA are expressed as follows: Firstly, the diversity of the population is enhanced through LHS population initialization. Secondly, the adaptive weight strategy is introduced to accelerate the convergence speed of the algorithm. Finally, the population is disturbed by Gaussian mutation or Levy flight to jump out of the local optimum. The standard deviation of cluster heads’ residual energy in intercluster communication is taken as the objective function to search the energy-balanced intercluster data forwarding path based on ISCA. Compared with EEUC, DEBUC, I-EEUC, and M-DEBUC, the simulation results prove that DUCISCA can effectively balance the overall network energy consumption and prolong the network lifetime.

An Optimal Scheme for WSN Based on Compressed Sensing

Wireless sensor networks (WSNs) is one of the renowned ad hoc network technology that has vast varieties of applications such as in computer networks, bio-medical engineering, agriculture, industry and many more. It has been used in the internet-of-things (IoTs) applications. A method for data collecting utilizing hybrid compressive sensing (CS) is developed in order to reduce the quantity of data transmission in the clustered sensor network and balance the network load. Candidate cluster head nodes are chosen first from each temporary cluster that is closest to the cluster centroid of the nodes, and then the cluster heads are selected in order based on the distance between the determined cluster head node and the undetermined candidate cluster head node. Then, each ordinary node joins the cluster that is nearest to it. The greedy CS is used to compress data transmission for nodes whose data transmission volume is greater than the threshold in a data transmission tree with the Sink node as the root node and linking all cluster head nodes. The simulation results demonstrate that when the compression ratio is set to ten, the data transfer volume is reduced by a factor of ten. When compared to clustering and SPT without CS, it is reduced by 75% and 65%, respectively. When compared to SPT with Hybrid CS and Clustering with hybrid CS, it is reduced by 35% and 20%, respectively. Clustering and SPT without CS are compared in terms of node data transfer volume standard deviation. SPT with Hybrid CS and clustering with Hybrid CS were both reduced by 62% and 80%, respectively. When compared to SPT with hybrid CS and clustering with hybrid CS, the latter two were reduced by 41% and 19%, respectively.

Trust Mechanism of Feedback Trust Weight in Multimedia Network

It is necessary to solve the inaccurate data arising from data reliability ignored by most data fusion algorithms drawing upon collaborative filtering and fuzzy network theory. Therefore, a model is constructed based on the collaborative filtering algorithm and fuzzy network theory to calculate the node trust value as the weight of weighted data fusion. First, a FTWDF (Feedback Trust Weighted for Data Fusion) is proposed. Second, EEFA (Efficiency unequal Fuzzy clustering Algorithm ) is introduced into FTWDF considering the defects of the clustering structure caused by ignoring the randomness of node energy consumption and cluster head selection in the practical application of the existing data fusion algorithm. Besides, the fuzzy logic is applied to cluster head selection and node clustering. Finally, an FTWDF-EEFA clustering algorithm is constructed for generating candidate cluster head nodes, which is verified by simulation experiments. The comparative analysis reveals that the accuracy of the FTWDF-EEFA clustering algorithm is 4.1% higher than that of the TMDF (Trust Multiple attributes Decision-making-based data Fusion) algorithm, and 8.3% higher than that of LDTS ( Larger Data fusion based on node Trust evaluation in wireless Sensor networks) algorithm. It performs better in accuracy and recommendation results during the processing of ML100M dataset and NF5M dataset. Besides, the new clustering algorithm increases the survival time of nodes when analyzing the number of death nodes to prolong networks’ lifespan. It improves the survival period of nodes, balances the network load, and prolongs networks’ lifespan. Furthermore, the FTWDF-EEFA clustering algorithm can balance nodes’ energy consumption and effectively save nodes’ overall energy through analysis. Therefore, the optimized algorithm can increase the lifespan of network and improve the trust mechanism effectively. The performance of the algorithm has reached the expected effect, providing a reference for the practical application of the trust mechanism in networks.

An Energy-Efficient Dynamic Clustering Protocol for Event Monitoring in Large-Scale WSN

As a key technology, clustering has been an effective way in large-scale wireless sensor networks (WSNs) to extend the lifetime. However, the static cluster structure in most of the traditional method is formed without considering the development of the event. In this paper, we propose an Energy-Efficient Dynamic Clustering (EEDC) protocol for event monitoring applications in large scale WSN. In EEDC, a dynamic clustering method using Rough Fuzzy C-Means and Genetic algorithm (RFCM-GA) is designed. Firstly, the idea of fuzzy set and rough set in RFCM are used to form the overlapping cluster, which can guarantee the quality of coverage of the developing event. Secondly, we use GA to perform a parallel search in each cluster to find the optimal set of candidate cluster heads (CCHs). RFCM-GA can use its powerful global search capabilities and fast convergence speed to obtain the best clustering results. Simulation results demonstrate that EEDC has higher energy efficiency and prolongs the network lifetime compared to the existing approaches.

E-FUCA: enhancement in fuzzy unequal clustering and routing for sustainable wireless sensor network

With huge cheap micro-sensing devices deployed, wireless sensor network (WSN) gathers information from the region and delivers it to the base station (BS) for further decision. The hotspot problem occurs when cluster head (CH) nearer to BS may die prematurely due to uneven energy depletion resulting in partitioning the network. To overcome the issue of hotspot or energy hole, unequal clustering is used where variable size clusters are formed. Motivated from the aforesaid discussion, we propose an enhanced fuzzy unequal clustering and routing protocol (E-FUCA) where vital parameters are considered during CH candidate selection, and intelligent decision using fuzzy logic (FL) is taken by non-CH nodes during the selection of their CH for the formation of clusters. To further extend the lifetime, we have used FL for the next-hop choice for efficient routing. We have conducted the simulation experiments for four scenarios and compared the propound protocol’s performance with recent similar protocols. The experimental results validate the improved performance of E-FUCA with its comparative in respect of better lifetime, protracted stability period, and enhanced average energy.

Nonuniform Clustering of Wireless Sensor Network Node Positioning Anomaly Detection and Calibration

In order to detect and correct node localization anomalies in wireless sensor networks, a hierarchical nonuniform clustering algorithm is proposed. This paper designs a centroid iterative maximum likelihood estimation location algorithm based on nonuniformity analysis, selects the nonuniformity analysis algorithm, gives the flowchart of node location algorithm, and simulates the distribution of nodes with MATLAB. Firstly, the algorithm divides the nodes in the network into different network levels according to the number of hops required to reach the sink node. According to the average residual energy of nodes in each layer, the sink node selects the nodes with higher residual energy in each layer of the network as candidate cluster heads and selects a certain number of nodes with lower residual energy as additional candidate cluster heads. Then, at each level, the candidate cluster heads are elected to produce the final cluster heads. Finally, by controlling the communication range between cluster head and cluster members, clusters of different sizes are formed, and clusters at the level closer to the sink node have a smaller scale. By simulating the improved centroid iterative algorithm, the values of the optimal iteration parameters α and η are obtained. Based on the analysis of the positioning errors of the improved centroid iterative algorithm and the maximum likelihood estimation algorithm, the value of the algorithm conversion factor is selected. Aiming at the problem of abnormal nodes that may occur in the process of ranging, a hybrid node location algorithm is further proposed. The algorithm uses the ℓ2, 1 norm to smooth the structured anomalies in the ranging information and realizes accurate positioning while detecting node anomalies. Experimental results show that the algorithm can accurately determine the uniformity of distribution, achieve good positioning effect in complex environment, and detect abnormal nodes well. In this paper, the hybrid node location algorithm is extended to the node location problem in large‐scale scenes, and a good location effect is achieved.

Research and Design Based on Smart Street Lights Network and Adaptive Dimming Method

In order to respond to the call of "smart city" and combine the actual demand of urban road lighting, this paper studies and designs a network based on smart streetlights and adaptive dimming algorithm. In order to improve the efficiency of data transmission in the smart street lamp monitoring network, an unequal clustering topology control algorithm based on band network (UCTC-BN) is proposed to establish a band topology for data Communication. When selecting the cluster head nodes, the algorithm adopts the candidate cluster head mechanism, and the candidate cluster head determines the non-uniform competition radius according to the remaining energy of the nodes and the distance from the sink nodes, thus achieving the goal of balancing the network energy consumption. In view of the low intelligence of street lights dimming and poor energy saving effect, an improved self-adaptive dimming algorithm based on K-means++ and DAG-SVM is studied, the light and traffic volume are clustered and six-classification dimming model is constructed to realize the intelligent dimming of street lights. Experimental results show that the system can efficiently transmit environmental data and comprehensively adjust street lamp lights, effectively reducing street lamp power consumption.

Towards Network Lifetime Enhancement of Resource Constrained IoT Devices in Heterogeneous Wireless Sensor Networks.

The participating nodes in Wireless Sensor Networks (WSNs) are usually resource-constrained in terms of energy consumption, storage capacity, computational capability, and communication range. Energy is one of the major constraints which requires an efficient mechanism that takes into account the energy consumption of nodes to prolong the network lifetime. Particularly in the large scale heterogeneous WSNs, this challenge becomes more critical due to high data collection rate and increased number of transmissions. To this end, clustering is one of the most popular mechanisms which is being used to minimize the energy consumption of nodes and prolong the lifetime of the network. In this paper, therefore, we propose a robust clustering mechanism for energy optimization in heterogeneous WSNs. In the proposed scheme, nodes declare themselves as cluster head (CH) based on available resources such as residual energy, available storage and computational capability. The proposed scheme employs the multi criteria decision making technique named as Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) which allows the child nodes to select the optimal CH among several potential CH candidates. Moreover, we also propose mechanisms such as CH-acquaintanceship and CH-friendship in order to prolong the network lifetime. Simulation results show that our proposed scheme minimizes the control overhead, reduces the power consumption and enhances overall lifetime of the network by comparing with the most recent and relevant proposed protocol for WSNs.

C-EEUC: a Cluster Routing Protocol for Coal Mine Wireless Sensor Network Based on Fog Computing and 5G

In underground coal mine, the routing protocol in Wireless Sensor Network (WSN) based on fog computing can effectively achieve combination the monitoring task with the computing task, and provide the correct data forwarding path to meet the requirements of the aggregation and transmission of sensed information. However, the energy efficiency is still taken into account, especially, the unbalance of energy consumption. 5G is a technical system of high frequency and low frequency mixing, with characteristics of large capacity, low energy consumption and low cost. With the formal freeze on 5G NSA standards, 5G networks are one step closer to our lives. In this paper, a centralized non-uniform clustering routing protocol C-EEUC based on the residual energy and communication cost. The C-EEUC protocol considers all nodes as candidate cluster heads in the clustering stage and defines a weight matrix P. The value of the matrix elements takes into account the residual energy of nodes and the cost of communication between nodes and cluster heads, selected as the basis for the cluster head. When selecting a cluster head, each time a node with the largest weight is selected from a set of candidate cluster heads, other candidate cluster heads within the competition range abandon competition, and then updates the candidate cluster head set. Experimental results show that the protocol optimized in this paper can effectively extend the network life cycle.

Energy-Aware Clustering and Routing in Infrastructure Failure Areas With D2D Communication

The communication infrastructures are likely to fail, in the case of disasters like earthquakes and debris flow, resulting in blind areas and the inconvenience of residents’ communication. In this paper, we propose a novel scheme connecting these infrastructure failure areas, namely, an energy-aware device-to-device communication scheme (NEED). Our proposed scheme, taking advantage of clustering technology, connects users within the infrastructure failure areas that often have no direct access to the cellular network. Compared with the clustering used in traditional cases, we add the process of determining candidate cluster heads (CHs) before determining final CHs. Based on location and residual energy, the final CHs are selected in the candidate CHs, and dual CHs in the cluster run alternately to share the communication cost. Besides, a modified ant colony algorithm (MACA) is developed to increase routing efficiency. The simulation results show the effectiveness of our proposed NEED scheme in terms of energy consumption and energy balance, and demonstrate that the scheme significantly extends the lifetime of the whole network.

A UAV-assisted CH election framework for secure data collection in wireless sensor networks

With the advance of UAV-related technologies, UAV-assisted wireless communications such as UAV-assisted coverage extension, UAV-assisted relaying and UAV-assisted data distribution and collection are gathering a lot of interests from government and industry fields. More specifically, the UAV-assisted data distribution and collection is implemented as a UAV-based WSN (Wireless Sensor Network). In the UAV-based WSN, a CH (Cluster Head) plays a crucial role such as data collection from members, data transfer toward a UAV and data distribution from the UAV to its members. Due to the role of a CH, many attackers try to make their compromised nodes CHs. In the general CH election framework, since each node determines a CH role by itself, compromised nodes declare themselves as a CH regardless of their disqualification. Generally, a compromised CH consumes much more energy than a normal CH because it keeps fulfilling the CH role to deliver corrupted messages to the sink greedily. Inspired by this phenomenon, we propose a UAV-assisted CH election framework which collects residual energy of nodes, and employs them for electing new CHs and excluding the lowest energy nodes from CH candidates. Simulation results show that our framework outperforms the general CH election framework even when the number of compromised nodes is large. Concerning the CH election period, our framework provides better security and performance than the general CH election framework with a short CH election period. Besides, the variation of node compromise time had no impact on our framework’s superiority to the general CH election framework. Another simulation results show that the increase of UAV tour frequency enhances both security and performance of our framework. Our CH election framework is easily applied to a network of IoT devices because the IoT network also demands clustering for energy and data management efficiency. Our future work item is applying as many other CH election schemes as possible to our framework and comparing their security and performance.