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In this paper, two algorithms enhanced differential evolution (EDE) and adaptive EDE (AEDE) are proposed. The proposed algorithms improve the robustness of the IoT network without changing the degree distribution of nodes. The EDE algorithm maintains the diversity in a solution space through the tri-vector mutation operation and explores the hidden areas. The crossover phase makes the algorithm's convergence fast towards the global optima. The AEDE dynamically changes the probabilities of multiple operations of the EDE with the changing environment. Also, it maintains the balance between the diversity of solution space and the convergence speed through adaptive probabilities. The EDE performs 7.13%, 31.6% and 41.8% better as compared to GA, SA and HA, respectively. The AEDE outperforms the GA, SA and HA with 11%, 35.3% and 45.4% better efficiency, respectively. The proposed algorithms outperform existing algorithms in terms of robustness and convergence speed.

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Content uploaded by Nadeem Javaid

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... Based on the probability of nodes' degree, the connections are made with nodes that are already part of a network and have a high degree. Furthermore, the SFT is represented by graph theory and unweighted and undirected graphs are considered [12]. The robustness, which is the resilience of a network against attacks [13], is calculated based on percolation theory based measure proposed by Schneider et al. ...

... The SFNs are more suitable for IoT networks because they are resilient to random attacks. In recent years, a significant attention is paid to enhance the robustness of these networks against malicious attacks [12,68,131]. For the topology of SFNs, BA model is proposed in [57], that explains how the nodes are connected to form a network. ...

... If the obtain edges are independent then the degree of each edge is calculated (Lines 7-10). Moreover, for all the possible edges the difference of edge degree is calculated (Lines [11][12][13][14] and the minimum degree difference is selected (Line 15). The edges are swapped according to the minimum difference in edge degree and after each swap, the robustness is calculated (Lines [16][17][18][19][20][21][22]. ...

During the past few decades, the Internet of Things (IoT) has made remarkable progress in many real-world applications including healthcare, military, transportation, etc. Multiple sensor nodes are deployed in these _elds to get the required data. Different network topologies are used in IoT and scale-free is one of them. It is mostly preferred due to its robust behavior against random node removal, however, the network collapsed because of malicious attacks. Therefore, in this thesis, robustness of the scale-free networks is enhanced against malicious attacks through optimization. To achieve this, the edge's degree and nodes' distance based edge swap operations are used in the proposed Improved Scale-Free Networks (ISFNs) scheme. In the edge's degree based operation, nodes of similar degrees are linked. Moreover, the connections of the nearest nodes are made in distance based edge swap. These operations help to achieve a better onion-like structure without changing the degree distribution of the network. Therefore, the network becomes robust against malicious attacks. Moreover, no new links or nodes are added in the optimization process, therefore, no extra cost is incurred. Furthermore, to make the network more robust against realistic attacks, the variable attacks are considered. Simulation results of the proposed scheme are compared with ROSE and Simulated Annealing (SA) for different number of nodes. The proposed scheme outperforms the existing techniques for different numbers of nodes and against the low degree, high degree and random attacks. Moreover, ISFNs has 13% and 23% better network robustness as compared to ROSE and SA, respectively. Network Topology Evolution Scheme (NTES) is proposed to prevent the scale-free networks from random and malicious attacks. In this scheme, the network field is divided into two parts with uniformly distributed nodes. After the network's evolution, the nodes are linked with each other through one-to-many correspondence. The division of the network field is made by considering that a network is robust if its size is small. Moreover, to study the hierarchical changes in the degree of nodes, k-core decomposition is used. In addition, nodes' degrees and core based attacks are performed on the network to evaluate the performance of the proposed scheme. Furthermore, the network robustness is analyzed using three optimization techniques: Artificial Bee Colony (ABC), Bacterial Foraging Optimization (BFO) and Genetic Algorithm (GA). The techniques are compared with each other and a technique that efficiently optimizes the network to increase the robustness is selected. In the optimization process, we make use of three edge swap methods. Due to the edge swap, the network robustness is enhanced without changing the degree distribution, so the addition of nodes/links is not required to increase the robustness. Furthermore, NTES is compared with Barabasi Albert (BA) model and Hill Climbing (HC) algorithm against random and malicious attacks. The simulation results show that the proposed NTES optimized using GA outperforms BA and HC by 46.90% and 57.08%, respectively, in terms of robustness. In addition, the network robustness of Scale Free Networks (SFNs) is enhanced against the malicious attacks. For that purpose, initially, a parameterless optimization algorithm JAYA is used because it requires less computational efforts as compared to the heuristic techniques. Then, as the edge swap plays an important role to enhance the robustness of SFNs, therefore, the edge swaps are classified into three categories. For each category, effects on the network's topological parameters such as average shortest path length, assortativity and clustering coefficient are analyzed. Next, the robustness is enhanced with the addition of nodes in the maximum connected subgraphs and the protection of bridge edges maintain the network connectivity. Moreover, optimized network is analyzed for different attack strengths. In simulations, the comparison of JAYA is made with two existing algorithms: ROSE and Simulated Annealing (SA). The network optimized by JAYA has a better robustness against random and malicious attacks, as compared to the existing algorithms. Furthermore, among the edge swap categories, the degree dependent edge swap is better to increase the robustness of SFNs. Moreover, the addition of nodes into the maximum connected subgraphs enhances the robustness and the protection of bridge edges ensures the network connectivity in all the algorithms. Furthermore, the robustness against different attack strengths are analyzed and the results show that high attacks strength paralyzed the network more efficiently.

... Based on the probability of nodes' degree, the connections are made with nodes that are already part of a network and have a high degree. Furthermore, the SFT is represented by graph theory and unweighted and undirected graphs are considered [12]. The robustness, which is the resilience of a network against attacks [13], is calculated based on percolation theory based measure proposed by Schneider et al. ...

... The SFNs are more suitable for IoT networks because they are resilient to random attacks. In recent years, a significant attention is paid to enhance the robustness of these networks against malicious attacks [12,68,131]. For the topology of SFNs, BA model is proposed in [57], that explains how the nodes are connected to form a network. ...

In this synopsis, robustness of the Scale-Free Networks (SFNs) is enhanced against malicious attacks through optimization. To achieve this, the edge’s degree and nodes’ distance based edge swap operations are used in the proposed Improved Scale-Free Networks (ISFNs) scheme. In the edge’s degree based operation, nodes of similar degrees are linked. Moreover, connections of the nearest nodes are made in distance based edge swap. These operations help to achieve a better onion-like structure without changing the degree distribution of the network. Therefore, the network becomes robust against malicious attacks. Furthermore, to make the network robust against realistic attacks, the variable attacks are considered. Apart from that, a Network Topology Evolution Scheme (NTES) is proposed to prevent SFNs from random and malicious attacks. In this scheme, the network field is divided into two parts with uniformly distributed nodes. After the network’s evolution, the nodes are linked with each other through one-to-many correspondence. The division of the network field is made by considering that a network is robust if its size is small. Moreover, to study the hierarchical changes in the degree of nodes, k-core decomposition is used. In addition, nodes’ degrees and core based attacks are performed on the network to evaluate the performance of the proposed scheme. Furthermore, the network robustness is analyzed using three optimization techniques: Artificial Bee Colony (ABC), Bacterial Foraging Optimization (BFO) and Genetic Algorithm (GA). The techniques are compared with each other and a technique that efficiently optimizes the network to increase the robustness is selected. In the optimization process, we make use of three edge swap methods. Due to the edge swap, the network robustness
is enhanced without changing the degree distribution, so the addition of nodes/links is not required to increase the robustness. In addition, the network robustness of SFNs is enhanced against the malicious attacks. For that purpose, initially, a parameterless optimization algorithm JAYA is used because it requires less computational efforts as compared to the heuristic techniques. Then, as the edge swap plays an important role to enhance the robustness of SFNs, therefore, the edge swaps are classified into three categories. For each category, effects on the network’s topological parameters such as average shortest path length, assortativity and clustering coefficient are analyzed. Next,
the robustness is enhanced with the addition of nodes in the maximum connected subgraphs and the protection of bridge edges maintain the network connectivity. Moreover, optimized network is analyzed for different attack strengths.

... Online education is a massive recovery and expansion that differs from superficial unions and symbols. So there is a big difference between the two [5]. ...

Football is a sport that integrates competition, fitness, and entertainment, and its proportion in national development is rising. Under the background of big data, how to better carry out the innovation of physical education teaching mode is an important problem facing the current football reform. This paper mainly studies the effect of sports football teaching innovation on improving the ability of football education and students’ interest in football learning. This paper mainly uses the experimental method, the survey method, and the expert interview method to study the theory of football teaching and the model innovation under the big data. According to the basic principles and requirements, the questionnaires were distributed, filled out, and collected on the spot, and finally, the questionnaires were sorted and analyzed to draw conclusions. Finally, according to the experimental results, the average scores of the experimental students in group A were 8.2 and 73.5, respectively. The average scores of group B students were 81.6 and 74.3, respectively. The experimental group was significantly better than the control group in terms of test scores and performance levels, indicating that the use of new teaching methods combining big data and the Internet is of great significance.

... Yang et al. [45] proposed an intelligent trust cloud management method for secure and reliable communication in Internet of Medical Things (IoMT) systems. Qureshi et al. [46] proposed enhanced differential evolution (EDE) and adaptive EDE algorithms to effectively improve the topology robustness of the IoT network while keeping the node degree distribution unchanged. ...

With the wide application of the Internet of Things (IoT) in real world, the impact of the security on its development is becoming incrementally important. Recently, many advanced technologies, such as artificial intelligence (AI), computational intelligence (CI), and deep learning method, have been applied in different security applications. In intrusion detection system (IDS) of IoT, this paper developed an adaptive differential evolution based on simulated annealing algorithm (ASADE) to deal with the feature selection problems. The mutation, crossover, and selection processes of the self-adaptive DE algorithm are modified to avoid trapping in the local optimal solution. In the mutation process, the mutation factor is changed based on the hyperbolic tangent function curve. A linear function with generation is incorporated into the crossover operation to control the crossover factor. In the selection process, this paper adopts the Metropolis criterion of the SA algorithm to accept poor solution as optimal solution. To test the performance of the proposed algorithm, numerical experiments were performed on 29 benchmark functions from the CEC2017 and six typical benchmark functions. The experimental results indicate that the proposed algorithm is superior to the other four algorithms.

... Traditional systems use centralized servers as a communication medium for IoT devices. Due to the rapid increase in connected devices, The popularity of IoT is rising yearly and becoming the center of attraction among all industries [1]. It is estimated that in 2025, the number of IoT devices will increase from 8.5 to 20.4 billion [2]. ...

In this paper, Internet of Things (IoTs) devices are used for sensing the data through which the device owners earn revenue. Interested users can purchase data from IoT device owners, according to their demands. However, users are not confident about the quality of data they are purchasing. Moreover , the users do not rely on the device owner and are not willing to initiate data trading. Currently, data trading systems have many drawbacks, as they involve a third party, security and reputation mechanisms. Therefore, in this paper, IoTs and BlockChain (BC) are integrated to monetize IoT's data and provide trustful data trading. A BC based review system to monetize IoT's data trading is developed through Ethereum smart contracts. The review system encourages the owners to provide authentic data and solves the issues regarding data integrity, fake reviews and conflicts between entities. Reviews and ratings are stored in the BC database for providing a guarantee about the data quality to users. To maintain data integrity, we use an Advanced Encryption Standard (AES)-256 encryption technique to encrypt data. Moreover , an arbitrator entity is responsible to resolve conflicts between data owner and users. The incentive is provided to the users and arbitrators to increase user participation and honesty. Simulations are performed for the validation of our system. We examine the proposed model using three parameters: gas consumption, mining time and encryption time.

... The network topologies [51,52] provide layouts of various communication activities occurring inside the networks. The resilience of the network topologies against the attacks depends upon the arrangement of the nodes present in the networks. ...

Nowadays, the Internet of Things (IoT) provides benefits to humans in numerous domains by empowering the projects of smart cities, healthcare, industrial enhancement and so forth. The IoT networks include nodes, which deliver the data towards their destination. However, the removal of nodes due to malicious attacks affects the connectivity of the nodes in the networks. The ideal plan is to construct a topology, which maintains the nodes' connectivity after the attacks and subsequently increases the network robustness. Therefore, in this thesis, werst adopt two different mechanisms for the construction of a robust scale-free network. Initially, a Multi-Population Genetic Algorithm (MPGA) is used to overcome the premature convergence in GA. Then, an entropy based mechanism is used, which replaces the first solution of high entropy population with the best solution of low entropy population to improve the network robustness. Second, two types of edge swap mechanisms are introduced. The Efficiency based Edge Swap Mechanism (EESM) selects the pair of edges with high efficiency to increase the network robustness. The second edge swap mechanism named EESM-Assortativity transforms the network topology into an onion-like structure to achieve maximum connectivity between similar degree nodes in the network. The optimization of the network robustness is performed using Hill Climbing (HC) and Simulated Annealing (SA) methods. The simulation results show that the proposed MPGA Entropy has 9% better network robustness as compared to MPGA. Moreover, the proposed ESMs effectively increase the network robustness with an average of 15% better robustness as compared to HC and SA. Furthermore, they also increase the graph density as well as network's connectivity with high computational cost. Furthermore, we design a robust network to support the nodes' functionality for the topology optimization in the scale-free IoT networks. It is because the computational complexity of an optimization process increases the cost of the network. Therefore, in this thesis, the main objective is to reduce the computational cost of the network with the aim of constructing a robust network topology. Thus, four solutions are presented to reduce the computational cost of the network. First, a Smart Edge Swap Mechanism (SESM) is proposed to overcome the excessive randomness of the standard Random Edge Swap Mechanism (RESM). Second, a threshold based node removal method is introduced to reduce the operation of the edge swap mechanism when an objective function converges at a point. Third, multiple attacks are performed in the network to find the correlation among the measures, which are degree, betweenness and closeness centralities. Fourth, based on the third solution, the Heat Map Centrality (HMC) is introduced that finds the set of most important nodes from the network. The HMC damages the network by utilizing the information of two positively correlated measures. It helps to provide a good attack strategy for robust optimization. The simulation results demonstrate the efficacy of the proposed SESM mechanism. It outperforms the existing RESM mechanism by almost 4% better network robustness and 10% less number of swaps. Moreover, 64% removal of nodes helps to reduce the computational cost of the network. In addition, we also perform topology optimization using a new heuristic algorithm, named as Great Deluge Algorithm (GDA). Afterwards, four rewiring strategies are designed. The first strategy is based on the degree dissortativity, which performs rewiring if maximum connectivity among similar degree nodes is achieved. In second strategy, we propose a degree difference operation using degree dissortativity to make sure that the connected edges possess low dissortativity and degree difference. Whereas the other two strategies consider nodes' load capacity as well as improved GDA to maximize the network robustness. The effectiveness of the proposed rewiring strategies is evaluated through simulations. The results prove that the proposed strategies increase the network robustness up to 25% as compared to HC and SA algorithms. Besides, the strategies are also very effective in increasing the graph density and network connectivity. However, their computational time is high as compared to HC and SA.

Dependence can highly increase the vulnerability of interdependent networks under cascading failure. Recent studies have shown that a constant density of reinforced nodes can prevent catastrophic network collapses. However, the effect of reinforcing dependency links in interdependent networks has rarely been addressed. Here, we develop a percolation model for studying interdependent networks by introducing a fraction of reinforced dependency links. We find that there is a minimum fraction of dependency links that need to be reinforced to prevent the network from abrupt transition, and it can serve as the boundary value to distinguish between the first- and second-order phase transitions of the network. We give both analytical and numerical solutions to the minimum fraction of reinforced dependency links for random and scale-free networks. Interestingly, it is found that the upper bound of this fraction is a constant 0.088 01 for two interdependent random networks regardless of the average degree. In particular, we find that the proposed method has higher reinforcement efficiency compared to the node-reinforced method, and its superiority in scale-free networks becomes more obvious as the coupling strength increases. Moreover, the heterogeneity of the network structure profoundly affects the reinforcement efficiency. These findings may provide several useful suggestions for designing more resilient interdependent networks.

This study presents an ambient intelligence oriented, topological robustness scheme for internet of things (IoT). The scheme primarily exploits the underlying geometric properties of scale-free IoT networks for a substantial improvement of genetic algorithm (GA) based state of the art robustness techniques. The geometrically optimized GA (Go-GA) is subsequently extended to traditional heuristics algorithms by proposing their geometrically optimized variants. All three techniques are comparatively evaluated over a simulated scale-free IoT architecture employing Schneider R as metric of robustness. The study follows a data-driven approach where information about nodes and edges is pulled from a central big data server, and topological robustness of a given scale-free IoT is tested against existing benchmarks. The proposed scheme aims to achieve convergence to global optima and conserve computational costs by efficient edge swapping (EES) and node removal based thresholding (NRT). Performance evaluations show that Go-GA outperforms state of the art variants of GA by a margin of 20% for Schneider R. Traditional techniques of hill climbing algorithm (HCA), simulated annealing algorithm (SAA) and ROSE also improve by a margin of 11%, 12% and 14% respectively with consideration of geometric aspects. Moreover, as the network size increases, a mere decline of 7.6% in robustness R is observed for Go-GA as compared to 18% degradation for classical algorithms.

Internet of Things (IoT) is rapidly increasing day by day due to its involvement in many applications such as electric grids, biological networks, transport networks, etc. In complex network theory, the model based on Scale Free Networks (SFNs) is more suitable for IoT. The SFNs are robust against random attacks; however, vulnerable to malicious attacks. Furthermore, as the size of a network increases, its robustness decreases. Therefore, in this paper, we propose a novel topology evolution approach to enhance the robustness of SFNs. Initially, we divide the network area into upper and lower parts. The nodes are deployed equally in both parts and connected via one-to-many correspondence. The distribution is made because small sized networks are more robust against malicious attacks. Moreover, we use k-core decomposition to calculate the hierarchical changes in the nodes’ degree. In addition, the core-based and degree-based attacks are performed to analyze the robustness of SFNs. For the network optimization, we compare the Genetic Algorithm (GA) with Artificial Bee Colony (ABC) and Bacterial Foraging Algorithm (BFA). In the optimization process, the node’s distance based edge swap is performed to draw long links in the network because these links make the network more robust.

Underwater Wireless Sensor Networks (UWSNs) are an enabling technology for many applications in commercial, military, and scientific domains. In some emergency response applications of UWSN, data dissemination is more important, therefore these applications are handled differently as compared to energy-focused approaches, which is only possible when propagation delay is minimized and packet delivery at surface sinks is assured. Packet delivery underwater is a serious concern because of harsh underwater environments and the dense deployment of nodes, which causes collisions and packet loss. Resultantly, re-transmission causes energy loss and increases end-to-end delay ( D E 2 E ). In this work, we devise a framework for the joint optimization of sink mobility, hold and forward mechanisms, adoptive depth threshold ( d t h ) and data aggregation with pattern matching for reducing nodal propagation delay, maximizing throughput, improving network lifetime, and minimizing energy consumption. To evaluate our technique, we simulate the three-dimensional (3-D) underwater network environment with mobile sink and dense deployments of sensor nodes with varying communication radii. We carry out scalability analysis of the proposed framework in terms of network lifetime, throughput, and packet drop. We also compare our framework to existing techniques, i.e., Mobicast and iAMCTD protocols. We note that adapting varying d t h based on node density in a range of network deployment scenarios results in a reduced number of re-transmissions, good energy conservation, and enhanced throughput. Furthermore, results from extensive simulations show that our proposed framework achieves better performance over existing approaches for real-time delay-intolerant applications.

In this paper, a blockchain-based data sharing and access control system is proposed, for communication between the Internet of Things (IoT) devices. The proposed system is intended to overcome the issues related to trust and authentication for access control in IoT networks. Moreover, the objectives of the system are to achieve trustfulness, authorization, and authentication for data sharing in IoT networks. Multiple smart contracts such as Access Control Contract (ACC), Register Contract (RC), and Judge Contract (JC) are used to provide efficient access control management. Where ACC manages overall access control of the system, and RC is used to authenticate users in the system, JC implements the behavior judging method for detecting misbehavior of a subject (i.e., user). After the misbehavior detection, a penalty is defined for that subject. Several permission levels are set for IoT devices' users to share services with others. In the end, performance of the proposed system is analyzed by calculating cost consumption rate of smart contracts and their functions. A comparison is made between existing and proposed systems. Results show that the proposed system is efficient in terms of cost. The overall execution cost of the system is 6,900,000 gas units and the transaction cost is 5,200,000 gas units.

Nowadays, the Internet of Things enabled Underwater Wireless Sensor Network (IoT-UWSN) is suffering from serious performance restrictions, i.e., high End to End (E2E) delay, low energy efficiency, low data reliability, etc. The necessity of efficient, reliable, collision and interference-free communication has become a challenging task for the researchers. However, the minimum Energy Consumption (EC) and low E2E delay increase the performance of the IoT-UWSN. Therefore, in the current work, two proactive routing protocols are presented, namely: Bellman–Ford Shortest Path-based Routing (BF-SPR-Three) and Energy-efficient Path-based Void hole and Interference-free Routing (EP-VIR-Three). Then we formalized the aforementioned problems to accomplish the reliable data transmission in Underwater Wireless Sensor Network (UWSN). The main objectives of this paper include minimum EC, interference-free transmission, void hole avoidance and high Packet Delivery Ratio (PDR). Furthermore, the algorithms for the proposed routing protocols are presented. Feasible regions using linear programming are also computed for optimal EC and to enhance the network lifespan. Comparative analysis is also performed with state-of-the-art proactive routing protocols. In the end, extensive simulations have been performed to authenticate the performance of the proposed routing protocols. Results and discussion disclose that the proposed routing protocols outperformed the counterparts significantly.

Numerous fields have wide range of applications regarding Internet of Things (IoT) specially smart cities. Due to increasing day to day growth in number of applications, there is exponential rise in requirement of IoT devices. IoT network is becoming complex persistently which brings the significant challenge to the robustness of network topology. IoT is the backbone of smart cities due to inter connecting wide range of devices and converting a conventional city to smart city. To improve network topology robustness against targeted, malicious and intentional attacks have become a critical issue. To deal with the problem in this article, Enhanced Angle Sum Operation (EASO) ROSE is presented. Proposed scheme improves the robustness of network topology without effecting node degree distribution and scale-free property. Topology robustness is achieved by degree difference and angle sum operations. Extensive simulation results verify that our proposed scheme efficiently generate scale-free typologies for IoT in smart cities and significantly improve topology robustness against malicious and targeted attacks.

The Internet of Things (IoT) has developed rapidly in recent years where significant numbers of devices have been connected to the network and this will increase over the ensuing years. There is also a trend to shift the IoT topology from cloud computing to fog computing where computing logic is brought as nearest as possible to the sensors. One of the topologies that is commonly used in fog computing is the mesh network. In a mesh network, end point nodes/sensors are connected to other nodes with routing capabilities called routers, and these nodes are connected to gateways where the mesh can communicate with other meshes or clouds. In multi-gateway mesh networks, each gateway may have set of routers that is fully depended on the gateway to forward the data to the clouds. Mesh is a complex network structure and the overall performance of the networks can be affected by several issues, such as overload gateways, network latency and gateway failover. In this paper, we compare the ordinary Network Voronoi Diagram (NVD) with Hops Voronoi diagram (HVD) to distribute the gateway workload based on network hops; and extend these methods to identify the overlapped routers for gateway failover.

The extensive applications in the Internet of Things (IoT) have inspired a growing network scale. However, due to the resources-limited IoT devices and the numerous cyber-attacks against applications, maintaining the robustness and communication capabilities for the applications is increasingly challenging. In this paper, we consider IoT network topologies that provide robust communication for heterogeneous networks and study the networking stability of IoT devices and the intelligent evolution computing in network architectures. We explicate the network robustness problem both for the network architecture and the resistance to cyber-attacks. For the network architecture, we optimize the robustness of IoT network topology with a scale-free network model which has good performance in random attacks. In the case with the resistance to cyber-attacks, a deep deterministic policy learning (DDLP) algorithm is proposed to improve the stability for large scale IoT applications. Simulations show that the proposed algorithms greatly advance the robustness of IoT network topology compared to other algorithms, with a less computational cost.

In smart cities, the Internet of Things (IoT) consists of many low-power smart nodes. Its robustness is essential for protection of communication in data science against node failures caused by energy shortage or cyber-attacks. Scale-free networking topology, widely applied in IoT, is effectively resilient to random attacks but is vulnerable to malicious ones in which high-degree nodes are made to fail. The prohibitively high computational cost of existing robustness optimization algorithms is an obstacle to efficient topology self-optimization. To solve this problem, a novel robust networking model based on artificial intelligence is proposed to improve IoT topology robustness to protect its communication. Using the Back-Propagation neural network learning algorithm, the model extracts topology features from a dataset by supervised training. The experimental results show that the model achieves better prediction accuracy, thereby optimizing the topology with minimal computation overhead.

Wireless sensor networks (WSNs) have been the popular targets for cyberattacks these days. One type of network topology for WSNs, the scale-free topology, can effectively withstand random attacks in which the nodes in the topology are randomly selected as targets. However, it is fragile to malicious attacks in which the nodes with high node degrees are selected as targets. Thus, how to improve the robustness of the scale-free topology against malicious attacks becomes a critical issue. To tackle this problem, this paper proposes a Robustness Optimization scheme with multi-population Co-evolution for scale-free wireless sensor networKS (ROCKS) to improve the robustness of the scale-free topology. We build initial scale-free topologies according to the characteristics of WSNs in the real-world environment. Then, we apply our ROCKS with novel crossover operator and mutation operator to optimize the robustness of the scale-free topologies constructed for WSNs. For a scale-free WSNs topology, our proposed algorithm keeps the initial degree of each node unchanged such that the optimized topology remains scale-free. Based on a well-known metric for the robustness against malicious attacks, our experiment results show that ROCKS roughly doubles the robustness of initial scale-free WSNs, and outperforms two existing algorithms by about 16% when the network size is large.