ABLE: Meta-Path Prediction in Heterogeneous Information Networks

Given a heterogeneous information network (HIN) H, a head node h , a meta-path P, and a tail node t , the meta-path prediction aims at predicting whether h can be linked to t by an instance of P. Most existing solutions either require predefined meta-paths, which limits their scalability to schema-rich HINs and long meta-paths, or do not aim at predicting the existence of an instance of P. To address these issues, in this article, we propose a novel prediction model, called ABLE, by exploiting the A ttention mechanism and B i L STM for E mbedding. Particularly, we present a concatenation node embedding method by considering the node types and a dynamic meta-path embedding method that carefully considers the importance and positions of edge types in the meta-paths by the Attention mechanism and BiLSTM model, respectively. A triplet embedding is then derived to complete the prediction. We conduct extensive experiments on four real datasets. The empirical results show that ABLE outperforms the state-of-the-art methods by up to 20% and 22% of improvement of AUC and AP scores, respectively.

Optimization of Clustering in Wireless Sensor Networks: Techniques and Protocols

Recently, Wireless Sensor Network (WSN) technology has emerged extensively. This began with the deployment of small-scale WSNs and progressed to that of larger-scale and Internet of Things-based WSNs, focusing more on energy conservation. Network clustering is one of the ways to improve the energy efficiency of WSNs. Network clustering is a process of partitioning nodes into several clusters before selecting some nodes, which are called the Cluster Heads (CHs). The role of the regular nodes in a clustered WSN is to sense the environment and transmit the sensed data to the selected head node; this CH gathers the data for onward forwarding to the Base Station. Advantages of clustering nodes in WSNs include high callability, reduced routing delay, and increased energy efficiency. This article presents a state-of-the-art review of the available optimization techniques, beginning with the fundamentals of clustering and followed by clustering process optimization, to classifying the existing clustering protocols in WSNs. The current clustering approaches are categorized into meta-heuristic, fuzzy logic, and hybrid based on the network organization and adopted clustering management techniques. To determine clustering protocols’ competency, we compared the features and parameters of the clustering and examined the objectives, benefits, and key features of various clustering optimization methods.

IOT MODEL WITH MULTIMODAL NODE ALLOCATION AND THE METHOD OF USING MOBILE HEAD NODES FOR DATA COLLECTION

Рассмотрена задача организации движения головных узлов (ГУ) в сети интернета вещей (ИВ) при неоднородном (мультимодальном) распределении узлов в зоне обслуживания. Предложен метод кластеризации неоднородной сети, позволяющий выделить кластеры (отличающиеся плотностью узлов) и выбирать скорость движения ГУ в соответствии с плотностью в каждом кластере. Метод основан на использовании алгоритма кластеризации DBSCAN, позволяет повысить эффективность использования подвижных ГУ и может быть применен при организации сбора данных в сети ИВ. The paper is devoted to the problem of organizing the movement of head nodes in the Internet of Things (IoT) network with a heterogeneous (multimodal) distribution of nodes in the service area. A method for clustering a heterogeneous network is proposed, which makes it possible to distinguish clusters that differ in the density of nodes and select the speed of movement of the head node in accordance with the density in each cluster. The proposed method is based on the use of the DBSCAN clustering algorithm and makes it possible to increase the efficiency of the use of mobile head nodes. The method can be applied in organizing data collection in the IoT network.

A Data Collection Strategy for Heterogeneous Wireless Sensor Networks Based on Energy Efficiency and Collaborative Optimization

In the clustering routing protocol, prolonging the lifetime of the sensor network depends to a large extent on the rationality of the cluster head node selection. The selection of cluster heads for heterogeneous wireless sensor networks (HWSNs) does not consider the remaining energy of the current nodes and the distribution of nodes, which leads to an imbalance of network energy consumption. A strategy for selecting cluster heads of HWSNs based on the improved sparrow search algorithm- (ISSA-) optimized self-organizing maps (SOM) is proposed. In the stage of cluster head selection, the proposed algorithm establishes a competitive neural network model at the base station and takes the nodes of the competing cluster heads as the input vector. Each input vector includes three elements: the remaining energy of the node, the distance from the node to the base station, and the number of neighbor nodes of the node. The best cluster head is selected through the adaptive learning of the improved competitive neural network. When selecting the cluster head node, comprehensively consider the remaining energy, the distance, and the number of times the node becomes a cluster head and optimize the cluster head node selection strategy to extend the network life cycle. Simulation experiments show that the new algorithm can reduce the energy consumption of the network more effectively than the basic competitive neural network and other algorithms, balance the energy consumption of the network, and further prolong the lifetime of the sensor network.

Cluster Formation and Maintenance Techniques for Mobile Adhoc Networks with Improved Quality of Service

— In an Adhoc Network, every node is mobile and self-contained. As these networks lack infrastructure, highly adaptive algorithms are required to deal with frequent mobility changes by member nodes as well as Cluster Head (CH) nodes. The weighted clustering algorithms contribute significantly to cluster-based routing. In these algorithms, the selection of cluster heads is the most important task. In weighted clustering methods, the selected CH did their best to serve the network. However, the CH may become overloaded due to the arrival of nodes greater than their desired threshold value. In this case, the CH can become a bottleneck as it is unable to cope with rapidly increasing loads which ultimately degrade the network performance. In this paper, we address three network issues (i) Member Node movement (ii) Cluster head Node movement, and (iii) Overload at the Cluster head node caused due to mobility of nodes. Our proposed method Cluster Formation and Maintenance Techniques for Mobile Adhoc Networks with Improved Quality of Service (CFMIQS) include various adaptive algorithms to provide solutions to deal with these network issues and improve network Quality of Service (QoS). The Simulated Results are compared with the K-means AODV algorithm, the results showed better Packet Delivery Fraction (PDF) and Throughput values. Keywords— Cluster partition, MANET, Primary Cluster head, QoS, Secondary Cluster head

Head Node Selection Algorithm in Cloud Computing Data Center

Cloud computing provides multiple services such as computational services, data processing, and resource sharing through multiple nodes. These nodes collaborate for all prementioned services in the data center through the head/leader node. This head node is responsible for reliability, higher performance, latency, and deadlock handling and enables the user to access cost-effective computational services. However, the optimal head nodes’ selection is a challenging problem due to consideration of resources such as memory, CPU-MIPS, and bandwidth. The existing methods are monolithic, as they select the head nodes without taking the resources of the nodes. Still, there is a need for the candidate node which can be selected as a head node in case of head node failure. Therefore, in this paper, we proposed a technique, i.e., Head Node Selection Algorithm (HNSA), for optimal head node selection from the data center, which is based on the genetic algorithm (GA). In our proposed method, there are three modules, i.e., initial population generation, head node selection, and candidate node selection. In the first module, we generate the initial population by randomly mapping the task on different servers using a scheduling algorithm. After that, we compute the overall cost and the cost of each node based on resources. In the second module, the best optimal nodes are selected as a head node by applying the genetic operations such as crossover, mutation, and fitness function by considering the available resources. In the selected optimal nodes, one node is chosen as a head node and the other is considered as a candidate node. In the third module, the candidate node becomes the head node in the case of head node failure. The proposed method HNSA is compared against the state-of-the-art algorithms such as Bees Life Algorithm (BLA) and Heterogeneous Earliest Finished Time (HEFT). The simulation analysis shows that the proposed HNSA technique performs better in terms of execution time, memory utilization, service level sgreement (SLA) violation, and energy consumption.

A Novel Energy-Efficient Clustering Algorithm for More Sustainable Wireless Sensor Networks Enabled Smart Cities Applications

Wireless Sensor Networks (WSNs) is a major sensing technology that has revolutionized the way information is collected, processed, and used in many smart cities’ applications that rely on sensing technologies for event detection and monitoring. Despite the multiple benefits that such technology offers, the quick depletion of sensors’ battery power represents a major concern, mainly due to the extensive computational tasks and communication operations performed by individual sensors. Indeed, the cost of replacing batteries can be prohibitively expensive, especially when sensors are deployed in areas where access is difficult, in urbanized cities. To extend sensors’ lifetime, this paper proposes a new variant of LEACH protocol named LEACH enhanced with probabilistic cluster head selection (LEACH-PRO). LEACH-PRO introduces several measures to extend WSNs nodes’ lifetime such as cluster head node selection using a probabilistic function based on maximum residual energy and minimum distance to the sink. The obtained simulation results have proven the supremacy of LEACH-PRO over LEACH and direct transmission protocol in terms of the achieved network lifetime and the generated traffic overhead. Most importantly, LEACH-PRO will significantly extend the sensors’ lifetime, which would make this type of deployment more viable in smart city scenarios.

A Secure Truth Discovery for Data Aggregation in Mobile Crowd Sensing

With the rapid development of portable mobile devices, mobile crowd sensing systems (MCS) have been widely studied. However, the sensing data provided by participants in MCS applications is always unreliable, which affects the service quality of the system, and the truth discovery technology can effectively obtain true values from the data provided by multiple users. At the same time, privacy leaks also restrict users’ enthusiasm for participating in the MCS. Based on this, our paper proposes a secure truth discovery for data aggregation in crowd sensing systems, STDDA, which iteratively calculates user weights and true values to obtain real object data. In order to protect the privacy of data, STDDA divides users into several clusters, and users in the clusters ensure the privacy of data by adding secret random numbers to the perceived data. At the same time, the cluster head node uses the secure sum protocol to obtain the aggregation result of the sense data and uploads it to the server so that the server cannot obtain the sense data and weight of individual users, further ensuring the privacy of the user’s sense data and weight. In addition, using the truth discovery method, STDDA provides corresponding processing mechanisms for users’ dynamic joining and exiting, which enhances the robustness of the system. Experimental results show that STDDA has the characteristics of high accuracy, low communication, and high security.

Design and Comparison of LEACH and Improved Centralized LEACH in Wireless Sensor Network

A WSN consists of a setup of sensor nodes/motes which perceives the environment under monitoring, and transfer this information through wireless links to the Base Station (BS) or sink. The sensor nodes can be heterogeneous or homogeneous and can be mobile or stationary. The data gathered is forwarded through single/multiple hops to the BS/sink. In this paper, propose improvements to LEACH routing protocol to reduce energy consumption and extend network life. LEACH Distance Energy (LEACH-DE) not only selects the cluster head node by considering that the remaining energy of the node is greater than the average remaining energy level of the nodes in the network, but also selects the cluster head node parameters based on the geometric distance between the candidate node and the BS. The simulation results show that the algorithm proposed in this work is superior to LEACH and LEACH-C (Centralized) in terms of energy saving and extending the lifetime of wireless sensor networks.

Clock Synchronization using Truncated Mean and Whale Optimization for Clustered Sensor Networks

Clock synchronization is an important component in many distributed applications of wireless sensor networks (WSNs). The deprived method of clock offset and skew estimation causes inaccuracy, synchronization delay, and communication overhead in the protocols. Hence, this paper exploits two techniques of variation truncated mean (VTM) and whale optimization (WO) to enhance the synchronization metrics. Sensor nodes are grouped into several non-overlapped clusters. The cluster head collects the member nodes’ local time and computes the synchronization time 𝑆𝑍𝑡 using the truncated mean method. Nodes with a high variation in the timings compared to a preset value are truncated. The head node broadcasts the 𝑆𝑍𝑡 in which the whale optimization is aiming at each node to reduce the synchronization error. The intra and inter-cluster synchronizations are accomplished through the multihop message exchange approach. The theoretical analysis is validated, and the simulation outcomes show that the performance metrics in the proposed work are better than the conventional methods by achieving minimum error value.