Ordinary Nodes Research Articles

Energy-efficient and fault-tolerant drone-BS placement in heterogeneous wireless sensor networks

This paper introduces a distributed and energy-aware algorithm, called Minimum Drone Placement (MDP) algorithm, to determine the minimum number of base stations mounted on resource-rich Unmanned Aerial Vehicles (UAV-BS), commonly referred to as drone-BS, and their possible locations to provide fault tolerance with high network connectivity in heterogeneous wireless sensor networks. This heterogeneous model consists of resource-rich UAV-BSs, acting as gateways of data, as well as ordinary sensor nodes that are supposed to be connected to the UAV-BSs via multi-hop paths. Previous efforts on fault tolerance in heterogeneous wireless sensor networks attempt to determine transmission radii of the sensor nodes based on the already deployed base station positions. They assume that the base stations are stationary and arbitrarily deployed regardless of the position of the sensor nodes. Our proposed MDP algorithm takes into account the desired degree of fault tolerance and the position of ordinary sensor nodes to determine the optimal number of UAV-BSs and their locations. The MDP algorithm consists of two steps. In the first step, each sensor node chooses low-cost pairwise disjoint paths to a subset of candidate UAV-BSs, using an optimization based on the well-known set-packing problem. In the last step, depending on the desired degree of fault tolerance, MDP chooses a subset of these UAV-BS candidates using a novel optimization based on the well-known set-cover problem. Through extensive simulations, we demonstrate that the MDP achieves up to 40% improvement in UAV-connected lifetimes compared to a random and uniform distribution of UAV-BSs.

A transient composition tracking method for natural gas pipe networks

A transient composition tracking method is developed for natural gas pipe networks. It adopts the control volume method for hydraulic simulation and a one-dimensional unsteady heat transfer model for thermal simulation. In order to model the junction in the network, a special grid is proposed to connect all adjacent pipes together. Under this grid, the momentum equation could be discretized by the first-order upwind scheme while the mass equation could be discretized just like an ordinary pipe node. To track the composition, the advective concentration equation is given for each component. Therefore, the gas composition in the pipe network could be predicted in real time and its influence on the calculation of property parameters could be taken into consideration. Based on these models, a transient composition tracking algorithm is developed for natural gas pipe networks. It consists of three parts: transient flow simulation, composition tracking and parameters calculation. Case studies show that the proposed method can provide reliable results for transient flow simulation. Compared with the measured data, the composition outcomes given by this method are also very accurate. In addition, the impact of injecting LNG/H2 into the pipe network is quite complicated and should be treated with caution.

Resilient Range-Based d-Dimensional Localization for Mobile Sensor Networks

Knowledge of node locations is essential to Wireless Sensor Networks (WSNs) in a wide range of potential applications and their function-dependent network protocols. A number of localization approaches have already been proposed to fulfill this requirement, but few of them can be applicable to mobile sensor networks, due to their low-dimensional embeddings, Euclidean distance representation limitations, frequent node mobility and additional measurement overhead in the network. In this paper, a resilient range-based $d$ -dimensional localization (RRDL) approach is proposed for mobile WSNs to resolve the issues. RRDL distinguishes itself from previous work with three remarkable characteristics: (1) it works for mobile networks embedded in $d$ -dimensional Non-Euclidean space; (2) it allows static ordinary nodes with pre-known locations to act as the alternative anchor nodes, thus tolerating the motion of the original anchor nodes to ensure that other ordinary nodes can obtain their locations in an efficient manner; and (3) it introduces an efficient path-learning approach, with the knowledge of the existing paths, to represent the real network distances as far as possible, thereby eliminating additional measurement overhead and tolerating node mobility in localization. With these characteristics, RRDL exploits the iterative factorization of the random distance matrix, formed by the distances to and from a set of $k$ -hop static neighbors, to assign each current node $d$ -dimensional Non-Euclidean coordinate in a distributed manner. Simulation results demonstrate that RRDL achieves higher localization accuracy with a moderate communication cost in mobile sensor networks.

A Heterogeneous IoT Node Authentication Scheme Based on Hybrid Blockchain and Trust Value

Node identity authentication is an essential means to ensure the security of the Internet of Things. Existing blockchain-based IoT node authentication schemes have many problems. A heterogeneous IoT node authentication scheme based on an improved hybrid blockchain is proposed. Firstly, the hybrid blockchain model is designed to make the blockchain and IoT environment more compatible. Then the proxy node selection mechanism is intended to establish a bridge between the ordinary IoT node and the blockchain, building by calculating the trust value between nodes. Finally, based on the improved hybrid blockchain, the node authentication scheme of the model and proxy node selection mechanism establishes a secure connection for communication between nodes. Safety and performance analysis shows proper safety and performance.

RETRACTED ARTICLE: Enhancing the LTE-Based Intelligent Transportation System’s Performance

Intelligent transportation system is considered as one of the main features of the new generation of the wireless systems. Furthermore, both of high speed data transmission and processing play a crucial role for these generations. In this work, two main propositions have been detailed in order to attain an improvement in such intelligent systems performance and to enhance both of data transmission and processing speed. Thus, a proposed clustering algorithm will be presented for grouping mobile nodes based on their speeds besides the assignments of the head nodes that will not be updated every cycle, while the ordinary ones themselves will execute their attaching heads continuously. In order to enhance the speed of data transmission and processing, a parallel-processing technique is emphasized. This is based on a variety of wavelet baby functions to attain the target of increasing the speed with low complexity. In addition, the optimization of the transmitted power phenomenon, the Multiband orthogonal frequency division multiplexing is used to provide such privilege via the parallel processing criterion. There have been five main efficiency factors involved in this investigation; namely complementary cumulative distributions, bit rates, energy efficiency, the cluster head life time and the ordinary nodes reattaching-head average times.

Graph Neural Networks with Information Anchors for Node Representation Learning

In the era of big data, the large-scale information network applications need to process and analyze increasingly complex graph structure relationships. However, traditional methods of representing network structures are difficult to reflect potential relationships between massive nodes. Graph Neural Network (GNN) based node representation learning is an emerging learning paradigm that embeds network nodes into a low dimensional vector space by retaining as much the information of network topology and node content as possible. However, existing GNN approaches ignore the distinction among the positions of nodes with similar topologies, which is usually crucial for many network prediction and classification tasks. In this paper, we propose a novel Graph Neural Network model based on information anchors, called A-GNN, where these anchors are defined as the important nodes that have a lot of interactive information with other ordinary nodes. In our model, the vectors obtained by the node representation learning contain the location information of ordinary nodes related to anchors. In A-GNN, we first designed the selection strategy of the set of anchors. Then the distance computation process was defined for any given target node to each anchor. Finally, we proposed the learning schema of a non-linear distance-weighted aggregation over the anchors. Therefore A-GNN can obtain global position information of all ordinary nodes relative to the anchors. Our proposed A-GNN is suitable for various network prediction tasks such as link prediction and node classification. We have conducted comparative experiments on five datasets. Experimental results show that A-GNN outperforms current state-of-the-art models.

A Mobility-Assisted Localization Algorithm for Three-Dimensional Large-Scale UWSNs.

As one of the important facilities for marine exploration, as well as environment monitoring, access control, and security, underwater wireless sensor networks (UWSNs) are widely used in related military and civil fields, since the sensor node localization is the basis of UWSNs’ application in various related fields. Therefore, the research of localization algorithms based on UWSNs has gradually become one of the research hotspots today. However, unlike terrestrial wireless sensor networks (WSNs), many terrestrial monitoring and localization technologies cannot be directly applied to the underwater environment. Moreover, due to the complexity and particularity of the underwater environment, the localization of underwater sensor nodes still faces challenges, such as the localization ratio of sensor nodes, time synchronization, localization accuracy, and the mobility of nodes. In this paper, we propose a mobility-assisted localization scheme with time synchronization-free feature (MALS-TSF) for three-dimensional (3D) large-scale UWSNs. In addition, the underwater drift of the sensor node is considered in this scheme. The localization scheme can be divided into two phases. In Phase I, anchor nodes are distributed in the monitoring area, reducing the monitoring cost. Then, we address a time-synchronization-free localization scheme, to obtain the coordinates of the unknown sensor nodes. In Phase II, we use the method of two-way TOA to locate the remaining ordinary sensor nodes. The simulation results show that MALS-TSF can achieve a relatively high localization ratio without time synchronization.

Privacy preserving solution for the asynchronous localization of underwater sensor networks

Location estimation of underwater sensor networks &#x0028 USNs &#x0029 has become a critical technology, due to its fundamental role in the sensing, communication and control of ocean volume. However, the asynchronous clock, security attack and mobility characteristics of underwater environment make localization much more challenging as compared with terrestrial sensor networks. This paper is concerned with a privacy-preserving asynchronous localization issue for USNs. Particularly, a hybrid network architecture that includes surface buoys, anchor nodes, active sensor nodes and ordinary sensor nodes is constructed. Then, an asynchronous localization protocol is provided, through which two privacy-preserving localization algorithms are designed to estimate the locations of active and ordinary sensor nodes. It is worth mentioning that, the proposed localization algorithms reveal disguised positions to the network, while they do not adopt any homomorphic encryption technique. More importantly, they can eliminate the effect of asynchronous clock, i.e., clock skew and offset. The performance analyses for the privacy-preserving asynchronous localization algorithms are also presented. Finally, simulation and experiment results reveal that the proposed localization approach can avoid the leakage of position information, while the location accuracy can be significantly enhanced as compared with the other works.

Multiparallel processing for enhancing the V2V clustering performance

SummaryIn order to support the demand of the users in the new generation wireless systems (such as the smart interaction between the mobile devices and the mobile human), the resources of the intelligent transportation system should be utilized efficiently. Thus, the network structure adaptation due to the rapid changes in such communication process should be investigated. This is in addition to save the direct impact of the network resources such as high speed data transmission and data processing. Based on the standards for both of vehicular ad hoc network (VANET) and IEEE 802.11p, two main propositions have been examined to attain the improvement in such intelligent systems performance. In this paper, we improved the QoS and proposed a clustering algorithm named MP‐RC‐MBOFDM. It groups the mobile nodes based on their speeds with some modified head assignments processes. This will be combined with a parallel‐processing technique and a wavelet processing stage to optimize the transmitted power phenomenon.The simulation results of five main efficiency factors that have been involved, namely, complementary cumulative distributions, bit rates, energy efficiency, the cluster head life time, and the ordinary nodes reattaching‐head average times, show the feasibility of the new proposition in the V2V data transmission.

Evolutionary Aggregation Approach for Multihop Energy Metering in Smart Grid for Residential Energy Management

The communication infrastructure is an important part to provide the reliability for energy management in the smart grid environment. With the aim of reducing the infrastructure cost for residential energy management, this article introduces a more complex multihop wireless remote metering network model. A novel evolutionary aggregation algorithm (EAA) is proposed to obtain the minimum number and locations of the local data centers (powerful nodes) in a 2-hop wireless remote metering network which has an arbitrary number of smart meters (ordinary nodes) with arbitrary transmission ranges. In the novel 2-hop EAA, the article designs and implements two novel adaptive operations (the switch operation and the shuffle operation) to improve the algorithm performance. Then the article extends the 2-hop EAA method to a more generic n-hop EAA which could obtain the optimal result in an n-hop (n > 2) smart meter network. Comprehensive case studies and numerical statistical analyses demonstrate that the EAA could efficiently achieve the optimal results in an n-hop (n> = 2) smart meter network environment; and the novel switch and shuffle operations could efficiently improve the performance of the evolutionary algorithm. The connectivity of the smart meter network could be fulfilled with the minimum number of the powerful nodes, from which the infrastructure cost for residential energy network could be minimized.

An Efficient Double-Layer Blockchain Method for Vaccine Production Supervision.

A vaccine is a biological product which is an important means for human beings to protect themselves. Most of its users are young children with weak immunity. Once a vaccine has a problem, it will pose a serious threat to the lives of many people. At present, the supervision of vaccine production is very simple. The vaccine production record is completely controlled by the enterprises. Enterprises only submit production records to the supervisory agency for review when the vaccine needs to be sold. Production records are easily forged and modified. In order to solve the shortcomings of traditional centralized management. We propose a supervision method for vaccine production based on double-level blockchain. At first, we have designed a double-level blockchain structure. The first level is private data of vaccine prduction enterprise, including production records and corresponding hash. The next level is public data, including production records hash and vaccine information. In this way, we make vaccine enterprise to submit production records in a timely manner without fear of privacy leaks. We avoid enterprise tampering or falsification of production records through the non-tampering features and time stamps of the blockchain. To improve the time efficiency, we propose a consensus mechanism for multi-node cooperate. The primary supervisory node provides sorting services and verifies the correctness of the blockchain replica. The ordinary supervisory node can replace the primary supervisory node when necessary, and help the primary supervisory node recovers data in case of information loss. The review node is responsible for providing complete and correct blockchain copies for other nodes. So we can avoids the problem of waste of time resources in the traditional blockchain system. In addition, in order to avoid the waste of space caused by the redundancy of the blockchain, we propose a vaccine data cutting mechanism. We use the timestamp of the blockchain and the vaccine validity period to determine if the block can be cutted. At the same time, it is also possible to judge whether the block can be cutted based on the information exchange with the vaccination institution. Through these methods, we have realized spatiotemporal efficiency supervision of vaccine production. And for the time being, research work in the field of vaccine production supervision is still very rare. So Our work is ground-breaking.

An analytical model for churn process in Bitcoin network with ordinary and relay nodes

Dynamic participation of nodes, also known as churn, is quite common in P2P networks, and Bitcoin network is no exception. The impact of churn becomes more pronounced when nodes with different roles and functionality exist. In this paper we analyze node churn in Bitcoin network where ordinary nodes generate transaction traffic and distribute and verify blocks, while relay nodes that just distribute blocks, but have a larger number of connections. We use the tools of probabilistic analysis and Markov chains, and show that churn of relay nodes has a higher impact on traffic performance than that of ordinary nodes. We introduce two different Continuous Time Markov chains (CTMC) for ordinary and relay nodes to model each type of node’s behavior separately. Our results indicate that node sleep impacts the performance more in networks where the proportion of relay nodes is lower, and also that block and transaction delivery times are higher in those cases. Also, we find that the time needed for a node to synchronize upon rejoining the network after sleep is dominated by the time required for block and transaction verification.

Modeling technique in the P-Graph framework for operating units with flexible input ratios

The P-Graph framework is an efficient tool that deals with the solution of Process Network Synthesis (PNS) problems. The model uses a bipartite graph of material and operating unit nodes, with arcs representing material flow. The framework includes combinatorial algorithms to identify solution structures, and an underlying linear model to be solved by the Accelerated Branch and Bound algorithmic method. An operating unit node in a P-Graph consumes its input materials and produces its products in a fixed ratio of operation volume. This makes it inadequate in modeling such real-world operations where input composition may vary, and may also be subject to specific constraints. Recent works address such cases by directly manipulating the generated mathematical model with linear programming constraints. In this work, a new general method is introduced which allows the modeling of operations with flexible input ratios and linear constraints in general, solely by tools provided by the P-Graph framework itself. This includes representing the operation with ordinary nodes and setting up their properties correctly. We also investigate how our method affects the solution structures for the PNS problem which is crucial for the performance of algorithms in the framework. The method is demonstrated in a case study where sustainable energy generation for a plant is present, and the different types of available biomass introduce a high level of flexibility, while consumption limitations may still apply.

Multi-hop nondestructive teleportation between terminal nodes equipped with limited technology

In this paper, we propose a scheme for the multi-hop nondestructive teleportation of an arbitrary single-qudit state via channel modulation performed by one cloud node in a network. Considering the limited quantum technology of the sender, the receiver, and the ordinary intermediate nodes, we assume that there are certain cloud nodes in a quantum network equipped with quantum processing capability of performing high-dimensional multi-qudit unitary operations. In our scheme, N intermediate nodes, involving one cloud node and N − 1 ordinary relay nodes, are connected in a chain through N + 1 different non-maximally entangled Bell pairs. Our scheme reduces the hardware requirement of the terminals, which makes it more flexible and adaptable in a network. In addition, our scheme enables preservation of the original single-qudit state, even if the teleportation fails. Our finding provides a methodology for the multi-hop nondestructive teleportation in a network with limited technical processing capabilities of terminals.

A differential game method against attacks in heterogeneous honeynet

The use of honeynet has become relatively common for security researchers and network operators to improve the network security. Thus, study of decision-making against attacks in a heterogeneous honeynet is significantly important for improving the design of honeynet and effectively preventing attacks. This paper establishes a dynamic node-evolutive model under attacks by considering the inherent characteristics and the functional interaction between ordinary nodes and honeypots, based on which the decision-making problem is modeled as a differential game. The existence of the saddle-point involved in the game is validated and the optimal dynamic strategies for the honeynet system and attackers are obtained through the proposed algorithm. The obtained optimal strategies are verified using several random strategies. The effects of the network topology and attack duration on the strategies for both sides and on the overall attack effect are evaluated. The key findings are: a) strategic decision-making should be closely related to the node degree. Specifically, the higher-degree node adapts attack or capture strategy in preference to the lower-degree one, in contrast to the patching strategy. b) a honeynet with higher power-law exponent is beneficial to eliminate attack effects, whereas a higher attack duration can aggravate these effects. The obtained results provide a theoretical foundation to improve the design of honeynet and restrain honeynet attacks.

Intra-clustering communication enhancement in WSN by using skillful methodologies

Member nodes in a wireless sensor networks are split into many virtual sets called cluster. Each cluster will be formed of a cluster head and cluster members. Clustering techniques are an energy efficient process that prolong the network lifetime. Intra-cluster communication is the energy efficient major factor in clustering protocols. The dissipated energy in intra-cluster will primary depends on the communication distance between member nodes and its cluster head. Therefore, reducing of energy that have been consumed in data transmission between ordinary nodes and its belonging cluster header, is consider as one of the important topics that have been taken into consideration by researchers. Clustering technique in WSN is one of the robust techniques that are utilizing to enhance consumed energy. Traditional clustering technique is not convenient with the animated nature of the WSN. A Fuzzy C-Mean Clustering (FCM) algorithm is proposed, this method has given momentum to improve network status and promote the state of sustainability of energy consumed by sensors nodes. Decision tree algorithm (DTA) which falls under the category of supervised machine learning algorithms is utilized to select Cluster Head (CH). The CH electing is done in such a way that reduces the intra-cluster communication distance; also, this method has limit the amount of consumed energy in the entire network.An event driven data model that depends on FCM with DTA which is named as Voronoi Fuzzy Clustering with Decision Tree Algorithm (VFDTA) is adopted in this study. The evaluation metrics the performance of VFDTA protocol is better than well-known protocols (LEACH, SEP and Z-SEP) in terms of network stability, consumed energy and network lifetime.

An AUV-Assisted Data Gathering Scheme Based on Clustering and Matrix Completion for Smart Ocean

The oceans cover more than 71% of the Earth’s surface and have a surging amount of data. It is of great significance to seek energy-effective and ultrareliable communication and transmission mechanism for effectively gathering abundant maritime data. In this article, we propose an autonomous underwater vehicle (AUV)-assisted data gathering scheme based on clustering and matrix completion (ACMC) to improve the data gathering efficiency in the underwater wireless sensor network (UWSN). Specifically, we first improve the $K$ -means algorithm by adopting the Elbow method to determine the optimal $K$ and setting a distance threshold to select the separate initial cluster centers. Then, we introduce a two-phase AUV trajectory optimization mechanism to effectively reduce the trajectory length of the AUV. In the first phase, the optimized trajectory of the AUV is planned by adopting the greedy algorithm. In the second phase, the ordinary nodes close to the AUV trajectory are selected as secondary cluster heads to share the workload of cluster heads. Finally, we present an in-cluster data collection mechanism based on matrix completion. An extensive experiment validates the effectiveness of our proposed scheme in terms of energy and data collection delay.

Seamless clustering multi-hop routing protocol based on improved artificial bee colony algorithm

An important issue in the mobile sink wireless sensor networks (MSWSNs) is sensor energy optimization. In order to alleviate the problem of unbalanced network load and high energy consumption in MSWSNs, we proposed a new data collection protocol in this paper: seamless clustering multi-hop routing protocol based on improved artificial bee colony algorithm (IABCP). Because of limited by the communication sensing range and intelligence of ordinary nodes, routing paths can only be constructed by crude methods. And the movement of the sink node will generate a large amount of energy consumption for locating the sink node. In order to solve this problem, we assign the task of routing table generation to the sink node, which will generate the routing table through the improved artificial bee colony algorithm. In addition, we adopt a new method to select cluster head (CH) nodes; node uses the average energy of the surrounding nodes and its own residual energy to calculate the claimed cluster head time. Moreover, we added a sub cluster head CH-β node. When the CH node reaches the number of replacement rounds, the CH-β node becomes CH directly. The simulation results show that our routing protocol is more robust compared with three other protocols.

Blockchain-Based Secure Data Storage for Distributed Vehicular Networks

In this paper, a blockchain-based secure data sharing mechanism is proposed for Vehicular Networks (VNs). Edge service providers are introduced along with ordinary nodes to efficiently manage service provisioning. The edge service providers are placed in the neighborhood of the ordinary nodes to ensure smooth communication between them. The huge amount of data generated by smart vehicles is stored in a distributed file storage system, known as Interplanetary File System (IPFS). It is used to tackle the issues related to data storage in centralized architectures, such as data tampering, lack of privacy, vulnerability to hackers, etc. Monetary incentives are given to edge vehicle nodes to motivate them for accurate and timely service provisioning to ordinary nodes. In response, ordinary nodes give reviews to the edge nodes against the services provided by them, which are further stored in a blockchain to ensure integrity, security and transparency. Smart contracts are used to automate the system processes without the inclusion of an intermediate party and to check the reviews given to the edge nodes. To optimize gas consumption and to enhance the system performance, a Proof of Authority (PoA) consensus mechanism is used to validate the transactions. Moreover, a caching system is introduced at the edge nodes to store frequently used services. Furthermore, both security and privacy are enhanced in the proposed system by incorporating a symmetric key cryptographic mechanism. A trust management mechanism is also proposed in this work to calculate the nodes’ reputation values based upon their trust values. These values determine the authenticity of the nodes involved in the network. Eventually, it is concluded from the simulation results that the proposed system is efficient for VNs.

Deployment and optimization of wireless network node deployment and optimization in smart cities

Followed by digital cities and smart cities, another advanced form of information city has emerged, namely smart cities. Such kind of city is integrated with informationization, industrialization and urbanization. Smart cities belong to the fusion of multiple information technologies such as the Internet of Things technology and cloud computing technology. Smart city is the use of various sensors and wireless networks, communication technologies to achieve information interaction. The use of cloud computing and big data effectively integrate information is to make comprehensive decisions on various data to achieve comprehensive coordination of city operation management and industrial development. In the wireless city infrastructure of smart cities, the deployment of network nodes directly affects the quality of network services. The problem can be attributed to the deployment of appropriate ordinary AP nodes as access nodes of wireless terminals on a given geometric plane. The deployment of special nodes as gateways will aggregate the traffic of ordinary nodes into the wired network Taking the wireless mesh network as an example, it is proposed to determine the deployment location and number of AP nodes based on the statistics of regional human traffic, and the gateway node deployment problem is seen as a geometric K-center problem. Taking the minimum path length between the node and the gateway as the optimization goal, an adaptive particle swarm optimization (APSO) algorithm is proposed to solve the gateway node deployment position. In the APSO algorithm, improved strategies such as random adjustment of inertia weights, adaptive change of learning factors, and neighborhood search are introduced. A new calculation method of the fitness function is designed to make the algorithm easier to obtain the optimal solution. Simulation results show that, compared with GA algorithm and K-means algorithm, the improved particle swarm algorithm has a stable solution effect, strong robustness, and can obtain a smaller coverage radius, thereby improving the network service quality.