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Robustness Optimization of Scale-Free IoT Networks
... • Impact of network topology Some basic topologies (e.g., point-to-point topology, star topology, and mesh topology) are commonly used in IoT networks (Beaulah Soundarabai and Chelliah, 2017). Many types of real-world network topologies, which consist of different combinations of basic network topologies, are adopted and used in IoT research (e.g., grid network (Yildiz et al., 2016;Anzola et al., 2017), random geometric graph network (Dhuli et al., 2021;Qurashi and Angelopoulos, 2020;Kenniche and Ravelomananana, 2010), scale-free network (Usman et al., 2020;Sohn, 2017), and smallworld network (Sohn, 2017)). In order to confirm the impact of network topologies in our simulator, we use our baseline scenario based on an office topology and consider four other topologies for comparison, including a grid network, random geometric graph (RGG) network, scale-free (SF) network, and smallworld (SW) network (as shown in Fig. 11). ...
p>The project is focusing on building a framework of IoT security. Our main goals are:
to model IoT attacks and defences in an emulated IoT networks; and
to evaluate the impact of different defence techniques on IoT attacks by using multiple security metrics.
This framework can create IoT networks with different settings and configurations on IoT devices and network topologies, model malware attacks against the emulated IoT networks and evaluate relevant defences . The simulation software is built with the aim to offer flexibility and adaptability to users. Also, it can be extended with new features and functionalities to simulate recent discovered malware attacks and defences.</p
... • Impact of network topology Some basic topologies (e.g., point-to-point topology, star topology, and mesh topology) are commonly used in IoT networks [67]. Many types of real-world network topologies, which consist of different combinations of basic network topologies, are adopted and used in IoT research (e.g., grid network [68], [69], random geometric graph network [70]- [72], scale-free network [73], [74], and small-world network [74]). In order to confirm the impact of network topologies in our simulator, we use our baseline scenario based on an office topology, and consider four other topologies for comparison, including a grid network, random geometric graph (RGG) network, scale-free (SF) network, and small-world (SW) network (as shown in Fig. 10). ...
p>The project is focusing on building a framework of IoT security. Our main goals are:
to model IoT attacks and defences in an emulated IoT networks; and
to evaluate the impact of different defence techniques on IoT attacks by using multiple security metrics.
This framework can create IoT networks with different settings and configurations on IoT devices and network topologies, model malware attacks against the emulated IoT networks and evaluate relevant defences . The simulation software is built with the aim to offer flexibility and adaptability to users. Also, it can be extended with new features and functionalities to simulate recent discovered malware attacks and defences.</p
... • Impact of network topology Some basic topologies (e.g., point-to-point topology, star topology, and mesh topology) are commonly used in IoT networks [67]. Many types of real-world network topologies, which consist of different combinations of basic network topologies, are adopted and used in IoT research (e.g., grid network [68], [69], random geometric graph network [70]- [72], scale-free network [73], [74], and small-world network [74]). In order to confirm the impact of network topologies in our simulator, we use our baseline scenario based on an office topology, and consider four other topologies for comparison, including a grid network, random geometric graph (RGG) network, scale-free (SF) network, and small-world (SW) network (as shown in Fig. 10). ...
p>The project is focusing on building a framework of IoT security. Our main goals are:
to model IoT attacks and defences in an emulated IoT networks; and
to evaluate the impact of different defence techniques on IoT attacks by using multiple security metrics.
This framework can create IoT networks with different settings and configurations on IoT devices and network topologies, model malware attacks against the emulated IoT networks and evaluate relevant defences . The simulation software is built with the aim to offer flexibility and adaptability to users. Also, it can be extended with new features and functionalities to simulate recent discovered malware attacks and defences.</p
... The node's distance is calculated in this edge swap and the nodes forming the long links are selected [134]. The formation of long links helps the low degree nodes to connect with the high degree nodes. ...
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.
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.
Instead of planting new electricity generation units, there is a need to design an efficient energy management system to achieve a normalized trend of power consumption. Smart grid has been evolved as a solution, where Demand Response (DR) strategy is used to modify the consumer's nature of demand. In return, utilities pay incentives to the consumer. This concept is equally applicable on residential and commercial areas; however, the increasing load demand and irregular electricity load profile in residential area have encouraged us to propose an efficient home energy management system for optimal scheduling of home appliances. Whereas, electricity consumers have stochastic nature, for which nature-inspired optimization techniques provide optimal solutions. However, these optimization techniques behave stochastically according to the situation. For this reason, we have proposed different optimization techniques for different scenarios. The objectives of this thesis include: reduction in electricity bill and peak to average ratio, minimization of waiting time to start appliances (comfort maximization) and minimization of wastage of surplus energy by exploiting the coordination among appliances and homes. In order to meet the electricity demand of the consumers, the energy consumption patterns of a consumer are maintained through scheduling the appliances in day-ahead and realtime bases. It is applicable by the defined fitness criterion for the proposed hybrid bacterial foraging genetic algorithm and hybrid elephant adaptive cuckoo search optimization techniques, which helps in balancing the load during On-peak and Off-peak hours. Moreover, the concept of coordination and coalition among home appliances is presented for real-time scheduling. The fitness criterion helps the scheduler to optimally decide the ON/OFF status of appliances in order to reduce the waiting time of the appliance. A multi-objective optimization based solution is proposed to resolve the trade-off between conflicting objectives: electricity bill, waiting time of appliances and electricity load shifting according to the defined electricity load pattern. Two optimization techniques: binary multiobjective bird swarm optimization and a hybrid of bird swarm and cuckoo search algorithms are proposed to obtain the Pareto front.
The main objective of DR is to encourage the consumer to shift the peak load and gets
incentives in terms of cost reduction. However, prices remain the same for all the users even if they shift the peak load or not. In this thesis, Game Theory (GT) based Time of Use
pricing model is presented to define the pricing strategy for On-peak and Off-peak hours.
The price is defined for each user according to the utilized load using coalitional GT. Further, the proposed pricing model is analyzed for scheduled and unscheduled load. In this regards, Salp swarm and rainfall algorithms are used for scheduling of appliances and an aggregated fitness criterion is defined for load shifting to avoid the peak rebound effect.
We also proposed the coordination and coalition based Energy Management System-as-a-
Service on Fog (EMSaaS_Fog). With the increase in number of electricity consumers, the
computational complexity of energy management system is becoming a threat for efficiency of a system in real-time environment. To deal with this dilemma, the utility shifts computational and storage units on cloud and fog. The proposed EMSaaS_Fog effectively handles the coalition among the apartments within a building to maintain balance between the demand and supply. Moreover, we consider a small community, which consists of multiple smart homes. Microgrid is installed at each residence for electricity generation. It is connected with the fog server to share and store information. Smart energy consumers are able to share detail of excess energy with each other through the fog server.
Technology has become inevitable in human life, especially the growth of Internet of Things (IoT), which enables communication and interaction with various devices. However, IoT has been proven to be vulnerable to security breaches. Therefore, it is necessary to develop fool proof solutions by creating new technologies or combining existing technologies to address the security issues. Deep learning, a branch of machine learning has shown promising results in previous studies for detection of security breaches. Additionally, IoT devices generate large volumes, variety, and veracity of data. Thus, when big data technologies are incorporated, higher performance and better data handling can be achieved. Hence, we have conducted a comprehensive survey on state-of-the-art deep learning, IoT security, and big data technologies. Further, a comparative analysis and the relationship among deep learning, IoT security, and big data technologies have also been discussed. Further, we have derived a thematic taxonomy from the comparative analysis of technical studies of the three aforementioned domains. Finally, we have identified and discussed the challenges in incorporating deep learning for IoT security using big data technologies and have provided directions to future researchers on the IoT security aspects.
In the promising time of the Internet, connected things have the ability to communicate and share information. The Internet of Things (IoT) cannot be implemented unless the security-related concerns have been resolved. Sharing information among different devices can compromise the private information of users. Thus, a suitable mechanism is needed to exclude the risk of malicious and compromised nodes. As follows, trust has been proposed in the literature as a useful technology to maintain users’ security. Prior studies have proposed diverse trust management mechanisms to achieve adequate trust. The approach of cross-domain trust management is neglected that requires enormous considerations to address the difficulties related to cross-domain communication. In this paper, a cross-domain robust distributed trust management (RobustTrust) system is proposed, which makes a device fit for assessing trust towards different devices locally. In this system, the trust is divided into three components of security that help IoT nodes to become robust against compromised and malicious devices/nodes. The novelty of the proposed mechanism can be summarized in these aspects: A highly scalable trust mechanism, multiple components of evaluation to enhance robustness against attacks, and use of recommendations along with the feedback to build knowledge. Furthermore, the proposed mechanism is event-driven that helps nodes to evaluate trust more effectively as well as enhance the system efficiency. The proposed work is compared with the available trust evaluation schemes by concentrating on various attributes, such as trustworthiness, usability, and accuracy among others. The RobustTrust is validated by the extensive simulations considering absolute trust value’s performance, the accuracy of trust estimation, and several potential attacks.
In this research, a new link-adding strategy (LLA) was proposed for large-scale wireless sensor networks (WSNs). It can enhance the network robustness and traffic capacity. Different from the previous link-adding strategies, local world theory is considered of LLA for better match the WSNs. Considering the transmission characteristics and energy consumption mechanism in WSNs, LLA is to use the relative position relationship and set division, establish the new link-adding strategy. In addition, for regulating some extreme cases in previous strategy, tunable parameters are introduced in LLA. In particularly, specific and classic experiments about \(R_{c}\), APL and ASHS, are used to demonstrate the effectiveness of the innovation. Simulation results show that LLA gets a better network robustness and stays a high traffic capacity compared with the previous strategies, e.g., IE strategy and LBF strategy. This work is helpful for designing the large-scale WSNs with more practice.
The Internet of Things has been applied in many fields, especially in smart cities. The failure of nodes brings a significant challenge to the robustness of topologies. The IoT of smart cities is increasingly producing a vast amount different types of data, which includes the node's geographic information, neighbor list, sensing data, and so on. Thus, how to improve the robustness of topology against malicious attacks based on big data of smart cities becomes a critical issue. To tackle this problem, this article proposes an approach to improve the robustness of network topology based on a multi-population genetic algorithm (MPGA). First, the geographic information and neighbor list of nodes are extracted from a big data server. Then a novel MPGA with a crossover operator and a mutation operator is proposed to optimize the robustness of topology. Our algorithm keeps the initial degree of each node unchanged such that the optimized topology will not increase the energy cost of adding edges. The extensive experiment results show that our algorithm can significantly improve the robustness of topologies against malicious attacks.
The Internet of Things is a novel cutting edge technology that proffers to connect a plethora of digital devices endowed with several sensing, actuation, and computing capabilities with the Internet, thus offering manifold new services in the context of a smart city. The appealing IoT services and big data analytics are enabling smart city initiatives all over the world. These services are transforming cities by improving infrastructure and transportation systems, reducing traffic congestion, providing waste management, and improving the quality of human life. In this article, we devise a taxonomy to best bring forth a generic overview of the IoT paradigm for smart cities, integrated ICT, network types, possible opportunities and major requirements. Moreover, an overview of the up-to-date efforts from standard bodies is presented. Later, we give an overview of existing open source IoT platforms for realizing smart city applications followed by several exemplary case studies. In addition, we summarize the latest synergies and initiatives worldwide taken to promote IoT in the context of smart cities. Finally, we highlight several challenges in order to give future research directions.
Complex networks are everywhere, such as the power grid network, the airline network, the protein-protein interaction network, and the road network. The networks are 'robust yet fragile', which means that the networks are robust against random failures but fragile under malicious attacks. The cascading failures, system-wide disasters and intentional attacks on these networks are deserving of in-depth study. Researchers have proposed many solutions to improve the robustness of these networks. However whilst many solutions preserve the degree distribution of the networks, little attention is paid to the community structure of these networks. We argue that the community structure of a network is a defining characteristic of a network which identifies its functionality and thus should be preserved. In this paper, we discuss the relationship between robustness and the community structure. Then we propose a 3-step strategy to improve the robustness of a network, while retaining its community structure, and also its degree distribution. With extensive experimentation on representative real-world networks, we demonstrate that our method is effective and can greatly improve the robustness of networks, while preserving community structure and degree distribution. Finally, we give a description of a robust network, which is useful not only for improving robustness, but also for designing robust networks and integrating networks.
Technological advancements in the silicon industry, as predicted by Moore's law, have enabled integration of billions of transistors on a single chip. To exploit this high transistor density for high performance, embedded systems are undergoing a transition from single-core to multi-core. Although a majority of embedded wireless sensor networks (EWSNs) consist of single-core embedded sensor nodes, multi-core embedded sensor nodes are envisioned to burgeon in selected application domains that require complex in-network processing of the sensed data. In this paper, we propose an architecture for heterogeneous hierarchical multi-core embedded wireless sensor networks (MCEWSNs) as well as an architecture for multi-core embedded sensor nodes used in MCEWSNs. We elaborate several compute-intensive tasks performed by sensor networks and application domains that would especially benefit from multi-core embedded sensor nodes. This paper also investigates the feasibility of two multi-core architectural paradigms—symmetric multiprocessors (SMPs) and tiled many-core architectures (TMAs)—for MCEWSNs. We compare and analyze the performance of an SMP (an Intel-based SMP) and a TMA (Tilera's TILEPro64) based on a parallelized information fusion application for various performance metrics (e.g., runtime, speedup, efficiency, cost, and performance per watt). Results reveal that TMAs exploit data locality effectively and are more suitable for MCEWSN applications that require integer manipulation of sensor data, such as information fusion, and have little or no communication between the parallelized tasks. To demonstrate the practical relevance of MCEWSNs, this paper also discusses several state-of-the-art multi-core embedded sensor node prototypes developed in academia and industry. We further discuss research challenges and future research directions for MCEWSNs.
Overlay network topology together with peer/data organization and search
algorithm are the crucial components of unstructured peer-to-peer (P2P)
networks as they directly affect the efficiency of search on such
networks. Scale-free (powerlaw) overlay network topologies are among
structures that offer high performance for these networks. A key problem
for these topologies is the existence of hubs, nodes with high
connectivity. Yet, the peers in a typical unstructured P2P network may
not be willing or able to cope with such high connectivity and its
associated load. Therefore, some hard cutoffs are often imposed on the
number of edges that each peer can have, restricting feasible overlays
to limited or truncated scale-free networks. In this paper, we analyze
the growth of such limited scale-free networks and propose two different
algorithms for constructing perfect scale-free overlay network
topologies at each instance of such growth. Our algorithms allow the
user to define the desired scalefree exponent (gamma). They also induce
low communication overhead when network grows from one size to another.
Using extensive simulations, we demonstrate that these algorithms indeed
generate perfect scale free networks (at each step of network growth)
that provide better search efficiency in various search algorithms than
the networks generated by the existing solutions.
Examining important results and analytical techniques, this graduate-level textbook is a step-by-step presentation of the structure and function of complex networks. Using a range of examples, from the stability of the internet to efficient methods of immunizing populations, and from epidemic spreading to how one might efficiently search for individuals, this textbook explains the theoretical methods that can be used, and the experimental and analytical results obtained in the study and research of complex networks. Giving detailed derivations of many results in complex networks theory, this is an ideal text to be used by graduate students entering the field. End-of-chapter review questions help students monitor their own understanding of the materials presented.
When an initial failure of nodes occurs in interdependent networks, a cascade of failure between the networks occurs. Earlier studies focused on random initial failures. Here we study the robustness of interdependent networks under targeted attack on high or low degree nodes. We introduce a general technique which maps the targeted-attack problem in interdependent networks to the random-attack problem in a transformed pair of interdependent networks. We find that when the highly connected nodes are protected and have lower probability to fail, in contrast to single scale-free (SF) networks where the percolation threshold pc = 0, coupled SF networks are significantly more vulnerable with pc significantly larger than zero. The result implies that interdependent networks are difficult to defend by strategies such as protecting the high degree nodes that have been found useful to significantly improve robustness of single networks.
Systems as diverse as genetic networks or the world wide web are best
described as networks with complex topology. A common property of many large
networks is that the vertex connectivities follow a scale-free power-law
distribution. This feature is found to be a consequence of the two generic
mechanisms that networks expand continuously by the addition of new vertices,
and new vertices attach preferentially to already well connected sites. A model
based on these two ingredients reproduces the observed stationary scale-free
distributions, indicating that the development of large networks is governed by
robust self-organizing phenomena that go beyond the particulars of the
individual systems.
In Industrial Internet of Things (IIoT), sensor nodes are vulnerable to withstand node failures due to energy exhaustion or external attacks, which leads to the low connectivity of networks. In this situation, how to improve network reliability has became a crucial problem. Adding a small amount of shortcuts to build a small world model in IIoT not only can reduce the delay, but also increases the reliability of networks. In this paper, we propose a Topology Optimization Scheme with Global Small World (TOSG) based on ant colony for IIoT. First, according to the number of appearing on the all shortest paths obtained by the ant colony optimization algorithm, we give the definition of importance for each node. The node with the highest importance in the communication range of a node is defined as an important node. We can find the important nodes in the network topology so that some shortcuts can be created between them to build a global small world model. The experiment results show that the TOSG model has a smaller average shortest path length and higher reliability compared with Greedy Model with Small World properties and Directed Angulation toward the Sink Node Model.
Recently, attack vulnerability of interdependent systems composed of multiple networks has attracted a great number of efforts. In this paper, how to improve the robustness of interdependent systems under attacks is addressed. With the aim of improving the robustness concerning malicious attacks on high-degree nodes, different from previous works on adjusting the coupling patterns between networks, new optimization models based on changing the inner structures of each network component are proposed. Through numerical simulations, the performance of optimization models is presented and compared with initial system without any optimization. It is found that the optimized onion structure of target network plays a positive role in improving attack robustness of the entire system. However, as more interdependent links are added between two networks, the existence of interdependent links greatly accelerates the collapse of both networks, just gradually neglecting the impact of optimized onion structure in resisting targeted attacks. Current results can help to deeply understand the structural complexity of real-world complex networks, as well as to provide practical principles in designing robust systems.
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.
Internet of Things (IoT) is one of the rising innovations of the current era that has largely attracted both the industry and the
academia. Life without the IoT is entirely indispensable. To dispel the doubts, if any, about the widespread adoption, the IoT certainly
necessitates both technically and logically correct solutions to ensure the underlying security and privacy. This paper explicitly
investigates the security issues in the perception layer of IoT, the countermeasures and the research challenges faced for large scale
deployment of IoT. Perception layer being one of the important layers in IoT is responsible for data collection from things and its
successful transmission for further processing. The contribution of this paper is twofold. Firstly, we describe the crucial components of
the IoT (i.e., architectures, standards, and protocols) in the context of security at perception layer followed by IoT security requirements.
Secondly, after describing the generic IoT-layered security, we focus on two key enabling technologies (i.e., RFID and sensor network)
at the perception layer. We categorize and classify various attacks at different layers of both of these technologies through taxonomic
classification and discuss possible solutions. Finally, open research issues and challenges relevant to the perception layer are
identified and analyzed.
An important challenge in complex networks is the improvement of network robustness. Electrical networks, water/gas networks and telecommunication networks are representative examples of infrastructures distributing critical resources for our society that require high level of robustness. In this paper, we propose a method based on Genetic Algorithms to enhance network robustness focusing on the protection of the link whose removal would severely increase the effective graph resistance. Derived from the field of electric circuit analysis, effective graph resistance is a robustness measure that can be computed as a cumulative sum of the inverses of the N − 1 largest eigenvalues of the Laplacian matrix associated with the network. Simulations on real-world and synthetic networks show that our method in most cases equals the exhaustive search and also outperforms other heuristic strategies.
The essential of existing methods for promoting network robustness is randomly exchanging the edges of networks. Without considering the network structure, the performance of these algorithms is limited. Therefore, we put forward a way for classifying the edges of networks into three types, which are valid edges, invalid edges and flexible edges. Then, a heuristic optimization algorithm, which is designed based on the edge classification (EC) against malicious attacks (MA), is proposed to improve the robustness of scale-free (RSF) network termed as EC-RSFMA. EC-RSFMA improves the robustness of scale-free networks by adjusting the number of edges of each type and changing the connection relation of the same type of edges under the constraint that the degree distribution remains to be unchanged. In the experiments, the performance of EC-RSFMA is validated on both synthetic and real-world networks. The results show that EC-RSFMA outperforms the existing algorithms.
Community structure is a natural and inherent property of complex networks which can reflect their potential functionality. When the robustness of a network is improved, its community structure should be preserved as much as possible. However, most earlier studies only considered enhancing the network robustness and ignored the analysis of the community structure, which may alter the original topological structure and functionality of networks. In this paper, we propose a new memetic algorithm (MA-CR) with a two-level learning strategy to enhance the community robustness of networks, while maintaining the degree distribution and community structure. The proposed MA-CR is a hybrid global-local heuristic search methodology which adopts genetic algorithm as the global search and the proposed two-level learning strategy as the local search. The two-level learning strategy is designed based on the potential characteristics of the node structure and community structure of networks, which aims at mitigating two-level targeted attacks. Experiments on synthetic scale-free networks as well as real-world networks demonstrate the effectiveness and stability of the proposed algorithm as compared with several state-of-the-art algorithms.
In wireless sensor networks, the sensor nodes are facing the random failure and the selective attacks all the time, which will cause partial or even entire network disintegrating. How to control the failures resulted from random failure or the selective attacks has become a hot topic in recent years. In this paper, we applied three matching models of capacity on three common kinds of wireless sensor network topology, and each model developed a profit function to defense cascading failures. Performances of the proposed matching models of capacity are evaluated using computer simulations. By studying the relationship between network investment and robustness, we find that NM model can defend against cascading failures better and requires a lower investment cost when higher robustness is required .The network performance analysis and the simulation results indicated that it can improve network robustness and invulnerability which are particularly important for the design of networks after applying this algorithm in the wireless sensor network. © 2012 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering.
Sink location privacy is one of the major issues in Wireless Sensor Networks (WSNs) where adversaries may locate the sink by observing the destination of packets, directing and scaling of data flow, which leads to exposure of the sink-location privacy. In this paper, we propose a Ring Based Routing (RBR) scheme to address the issue of sink-location privacy in WSNs. The RBR scheme is composed of multiple routing rings and routing lines where the nodal data is not directly sent to the sink but to the nearest routing ring. In our scheme, data is routed through each node in the ring and then sent to other routing rings via routing lines, where the number of anonymity sink nodes is equal to the total number of nodes in the network. More specifically, under the RBR scheme the routing rings move in irregular patterns which can confuse adversaries even if the sink location is static and this greatly improves the sink-location privacy. In addition, the routing rings are constructed according to comprehensive analysis of network energy, which can fully use the remaining energy and improve energy efficiency and network lifetime. Both theoretical analysis and simulation results show that our scheme can protect location privacy of sinks effectively.
Recently, Wireless Sensor Networks (WSNs) emerged as a powerful and cost-efficient solution for unattended Outdoor Environment Monitoring (OEM) applications. These applications impose certain challenges on WSN deployment, including 3-Dimensional (3-D) settings, harsh operational conditions, and limited energy resources. To prolong lifetime of the deployed WSN, while mitigating the effects of these challenges, we propose the use of Relay Nodes (RNs) in addition to Sensor Nodes (SNs) in a distributed manner. While RNs facilitate reaching distant destinations, SNs can reserve their limited energy resources for sensing and data gathering. In addition, Mobile RNs (MRNs), which is a set of RNs capable of being reallocated (i.e. mobilized) at any point within the network lifetime, can be used to overcome possible link/node failure caused by the harsh conditions. It can also guarantee minimal energy consumption through imposing a balanced traffic distribution. This article proposes a 3-D grid-based deployment for heterogeneous WSNs (consisting of SNs, RNs, and MRNs). The problem is cast as a Mixed Integer Linear Program (MILP) optimization problem with the objective of maximizing the network lifetime while maintaining certain levels of fault-tolerance and cost-efficiency. Moreover, an Upper Bound (UB) on the deployed WSN lifetime, given that there are no unexpected node/link failures, has been driven. Based on practical/harsh experimental settings in OEM, intensive simulations show that the proposed grid-based deployment scheme can achieve an average of of the expected UB.
Measuring robustness of complex networks is a fundamental task for analyzing the structure and function of complex networks. In this paper, we study the network robustness under the maximal vertex coverage (MVC) attack, where the attacker aims to delete as many edges of the network as possible by attacking a small fraction of nodes. First, we present two robustness metrics of complex networks based on MVC attack. We then propose an efficient randomized greedy algorithm with near-optimal performance guarantee for computing the proposed metrics. Finally, we conduct extensive experiments on 20 real datasets. The results show that P2P and co-authorship networks are extremely robust under the MVC attack while both the online social networks and the Email communication networks exhibit vulnerability under the MVC attack. In addition, the results demonstrate the efficiency and effectiveness of our proposed algorithms for computing the corresponding robustness metrics.
Overlay network topology together with peer/data organization and search algorithm are the crucial components of unstructured peer-to-peer (P2P) networks as they directly affect the efficiency of search on such networks. Scale-free (power-law) overlay network topologies are among structures that offer high performance for these networks. A key problem for these topologies is the existence of hubs, nodes with high connectivity. Yet, the peers in a typical unstructured P2P network may not be willing or able to cope with such high connectivity and its associated load. Therefore, some hard cutoffs are often imposed on the number of edges that each peer can have, restricting feasible overlays to limited or truncated scale-free networks. In this paper, we analyze the growth of such limited scale-free networks and propose two different algorithms for constructing perfect scale-free overlay network topologies at each instance of such growth. Our algorithms allow the user to define the desired scale-free exponent ( $gamma$). They also induce low communication overhead when network grows from one size to another. Using extensive simulations, we demonstrate that these algorithms indeed generate perfect scale free networks (at each step of network growth) that provide better search efficiency in various search algorithms than the networks generated by the existing solutions.
Data collection is a common operation of Wireless Sensor Networks (WSNs), of which the performance can be measured by its achievable network capacity. Most existing works studying the network capacity issue are based on the unpractical model called deterministic network model. In this paper, a more reasonable model, probabilistic network model, is considered. For snapshot data collection, we propose a novel Cell-based Path Scheduling (CPS) algorithm that achieves capacity of $(Omega ({1/ 5omega ln n} cdot W))$ in the sense of the worst case and order-optimal capacity in the sense of expectation, where $(n)$ is the number of sensor nodes, $(omega)$ is a constant, and $(W)$ is the data transmitting rate. For continuous data collection, we propose a Zone-based Pipeline Scheduling (ZPS) algorithm. ZPS significantly speeds up the continuous data collection process by forming a data transmission pipeline, and achieves a capacity gain of $(N sqrt{n}/ sqrt{log n} ln n)$ or $(n/ log n ln n)$ times better than the optimal capacity of the snapshot data collection scenario in order in the sense of the worst case, where $(N)$ is the number of snapshots in a continuous data collection task. The simulation results also validate that the proposed algorithms significantly improve network capacity compared with the existing works.
We develop a method to generate robust networks against malicious attacks, as well as to substantially improve the robustness of a given network by swapping edges and keeping the degree distribution fixed. The method, based on persistence of the size of the largest cluster during attacks, was applied to several types of networks with broad degree distributions, including a real network—the Internet. We find that our method can improve the robustness significantly. Our results show that robust networks have a novel 'onion-like' topology consisting of a core of highly connected nodes hierarchically surrounded by rings of nodes with decreasing degree.
We study the robustness of Barabási-Albert scale-free networks with respect to intentional attacks to highly connected nodes.
Using the simulated annealing optimization heuristic, we rewire the networks such that their robustness to network fragmentation
is improved but without changing neither the degree distribution nor the connectivity of single nodes. We show that simulated
annealing improves on the results previously obtained with a simple hill-climbing procedure. We also introduce a local move
operator in order to facilitate actual rewiring and show numerically that the results are almost equally good.
Networks of coupled dynamical systems have been used to model biological oscillators, Josephson junction arrays, excitable media, neural networks, spatial games, genetic control networks and many other self-organizing systems. Ordinarily, the connection topology is assumed to be either completely regular or completely random. But many biological, technological and social networks lie somewhere between these two extremes. Here we explore simple models of networks that can be tuned through this middle ground: regular networks 'rewired' to introduce increasing amounts of disorder. We find that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs. We call them 'small-world' networks, by analogy with the small-world phenomenon (popularly known as six degrees of separation. The neural network of the worm Caenorhabditis elegans, the power grid of the western United States, and the collaboration graph of film actors are shown to be small-world networks. Models of dynamical systems with small-world coupling display enhanced signal-propagation speed, computational power, and synchronizability. In particular, infectious diseases spread more easily in small-world networks than in regular lattices.
A Genetic Algorithm for Enhancing the Robustness of Complex Networks Through Link Protection
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