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The equilibrium use of energy is very important for wireless sensor networks (WSN) with limited energy in order to avoid premature network collapse. The existing methods either need too complex calculations for precise clustering, or are too simple to overburden a few cluster heads. In order to solve these problems, we proposed energy balanced clustering routing (EBCR) in this paper. It could maximize the WSN life in energy non-harvesting scenario or improve energy utilization efficiency in energy harvesting scenario without increasing the amount of calculations. It gives a complete solution to the process of cluster head election, clustering, and intercluster routing algorithm. Firstly, a light weight cluster head election and a distributed cluttering method are proposed by introducing dynamic cluster radius and intersection region node division schemes with new principles. Thus, lightweight distributed clustering achieves the advantages of balancing the burden of cluster heads and alleviating hot zone problem. Then we optimized the cluster cooperative routing algorithm by analyzing cooperation and competition among cluster heads. The intercluster cooperative routing algorithm greatly improves the transmission efficiency between cluster heads. Moreover, this paper analyzes the reasons why the algorithm achieves more balanced energy usage, higher energy efficiency, and fewer calculations compared to the existing mainstream algorithms. At last, simulation results show that EBCR algorithm has advantages in terms of network energy consumption, number of surviving nodes in energy non-harvesting scenario compared with the delay-constrained energy-efficient cluster-based multi-hop routing (DCEM) method. Simulation also gives EBCR algorithm performance under various energy harvesting scenarios, which is quite satisfactory in energy utilization efficiency comparing with DCEM method. EBCR algorithm has superior performance in terms of balanced energy usage, low computation complexity, and high energy efficiency. [ABSTRACT FROM AUTHOR]

In this paper, we propose a practical and scalable solution to mitigating interference on the uplink through static clustering and adaptive fractional frequency reuse (CFFR). The focus is on a three-cell clustered network due to its low complexity. Moreover, we have previously shown that a performance comparable to that of global coordination is achievable using a cluster size of three. In this paper, for a clustered planar Wyner network, we derive analytical capacity equations for zero forcing (ZF) and linear minimum mean squared error (LMMSE)-based receivers. The theoretical results show that inter-cluster interference is the major performance bottleneck and that the smallest interference from the neighbouring clusters is sufficient to significantly lower the system performance. We then switch our study to a more realistic network setting and augment our CFFR technique by adopting an entirely distributed architecture and by implementing a location classification algorithm based on logistic regression. We then show through simulations that CFFR performs significantly better than the widely studied dynamic clustering (DC) technique. Since the inter-cluster interference intensity of CFFR is much lower than DC, the per-cell sum rate performance is 1.5 × better, especially at high loads. We also show that the CFFR algorithm is a lot less complex than DC in terms of running time. [ABSTRACT FROM AUTHOR]

Deinterleaving or radar pulse separation is a very important goal in terms of radar sources for identifying and implementing electronic warfare systems. In order to separate radar pulses, parameters measured by electronic warfare receivers such as electronic warfare support measures (ESM) and electronic signals intelligence (ELINT) are used in pulse separation. This paper presents a multi-parameter improved method for separating the pulse sequence of radar signals based on time of arrival (TOA) processing with sorting the other pulse descriptor words (PDW) parameters. In the proposed method, after extracting all the pulse repetition intervals (PRIs) based on TOA, the parameters of the angle of arrival, pulse width and carrier frequency (RF) are being used in pulse sorting to separate the received interleaved pulse sequences. The sequential difference histogram (SDIF) algorithm or cumulative difference histogram (CDIF) algorithm is used to extract all pulse repetition intervals. Also, in order to separate the sequence of the received pulses from all surroundings emitters, in addition to matching the potential PRI among the TOAs of the pulses and the similarity measurement in the other parameters of the pulse sequence (pulse sorting) have been used. This proposed algorithm is implemented in the integrated and complete design for deinterleaving of the radar pulses. The proposed method by considering low-cost computing sources considers a fast and low-complexity solution that can be used for edge-enabled distributed processors in aerial radar platforms as edge devices for military/combat unmanned aerial vehicles or networked missiles. The simulation results show that our method is completely effective. [ABSTRACT FROM AUTHOR]

The energy harvesting methods enable WSNs nodes to last potentially forever with the help of energy harvesting subsystems for continuously providing energy, and storing it for future use. The energy harvesting techniques can use various potential sources of energy, such as solar, wind, mechanical, and variations in temperature. Energy-constrained sensor nodes are small in size. Therefore, some mechanisms are required to reduce energy consumption and consequently to improve the network lifetime. The clustering mechanism is used for energy efficiency in WSNs. In the clustering mechanism, the group of sensor nodes forms the clusters. The performance of the clustering process depends on various factors such as the optimal number of clusters formation and the process of cluster head selection. In this paper, we propose a hybrid whale and grey wolf optimization (WGWO)-based clustering mechanism for energy harvesting wireless sensor networks (EH-WSNs). In the proposed research, we use two meta-heuristic algorithms, namely, whale and grey wolf to increase the effectiveness of the clustering mechanism. The exploitation and exploration capabilities of the proposed hybrid WGWO approach are much higher than the traditional various existing metaheuristic algorithms during the evaluation of the algorithm. This hybrid approach gives the best results. The proposed hybrid whale grey wolf optimization-based clustering mechanism consists of cluster formation and dynamically cluster head (CH) selection. The performance of the proposed scheme is compared with existing state-of-art routing protocols. [ABSTRACT FROM AUTHOR]

With the rapid increase of user access, load balancing in cloud data center has become an important factor affecting cluster stability. From the point of view of green scheduling, this paper proposed a virtual machine intelligent scheduling strategy based on machine learning algorithm to achieve load balancing of cloud data center. Firstly, a load forecasting algorithm based on genetic algorithm (SVR_GA), k-means clustering algorithm based on optimized min-max, and adaptive differential evolution algorithm (ESA_DE) to enhance local search ability are proposed to solve the load imbalance problem in cloud data center. The experimental results showed that compared with other classical algorithms, the proposed virtual machine scheduling strategy reduces the number of virtual machine migration by 94.5% and the energy consumption of cloud data center by 49.13%. [ABSTRACT FROM AUTHOR]

Cognitive radio sensor networks (CRSNs) are multi-channel-capable networks that inherit some of the challenges of traditional wireless sensor networks (WSNs), such as limited power source and hardware capacity. In several CRSN applications, such as surveillance and intelligent transportation systems, node mobility is a typical assumption. However, as a node changes its physical location, spectrum mobility may also follow. Therefore, the treating of node mobility in CRSN imposes new challenges on all network layers, especially in the data link layer. In this paper, we propose a novel cross-layer mobility-aware medium access control (MAC) protocol for CRSN. We also propose an efficient spectrum-aware cluster formation and maintenance. The proposed scheme is more robust against primary users' activity as well as node mobility in a CRSN because it integrates spectrum sensing at the physical (PHY) layer with packet scheduling at the MAC layer. Simulation results show that the proposed protocol guarantees about 60 % more common channels per cluster in a higher node ratio. Moreover, the proposed MAC protocol outperforms existing protocols (e.g., CogMesh, cluster-based MAC, and KoN-MAC) in terms of the packet delivery ratio, energy consumption, and delay, by up to 5, 30, and 25 %, respectively. [ABSTRACT FROM AUTHOR]

Small cell systems are a cost-effective solution to provide adequate coverage inside buildings. Nonetheless, the addition of any indoor site requires evaluating the trade-off between the coverage and capacity gain provided by the new site and its monetary cost. In this paper, a new automatic indoor site selection algorithm based on clustering techniques is presented. The algorithm calculates the number of antennas, radio heads, and baseband units needed for the area under study. Then, a clustering algorithm groups several radio heads of different buildings in a single pooled baseband unit, reducing deployment costs. The proposed clustering algorithm is based on a local refinement algorithm, whose starting point considers a new baseband unit for every new site, and then, possible reallocation to existing units is checked. To assess the method, the proposed indoor site selection algorithm is included in a network planning tool. The algorithm is tested in a real heterogeneous network scenario, taking into account vendor specifications and operator constraints. Results show that the use of the proposed clustering algorithm can reduce the total network cost by up to 58 % in a real scenario. [ABSTRACT FROM AUTHOR]

In this paper, we propose Balanced Energy-Efficient Network Integrated Super Heterogeneous (BEENISH), improved BEENISH (iBEENISH), Mobile BEENISH (MBEENISH), and improved Mobile BEENISH (iMBEENISH) protocols for heterogeneous wireless sensor networks (WSNs). BEENISH considers four energy levels of nodes and selects cluster heads (CHs) on the base of residual energy levels of nodes and average energy level of the network, whereas iBEENISH dynamically varies the CHs selection probability in an efficient manner leading to increased network lifetime. We also present a mathematical sink mobility model and validate this model by implementing it in BEENISH (resulting in MBEENISH) and iBEENISH (resulting in iMBEENISH). Finally, simulation results show that BEENISH, MBEENISH, iBEENISH, and iMBEENISH protocols outperform contemporary protocols in terms of stability period, network lifetime, and throughput. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) were envisaged to become the fabric of our environment and society. However, they are yet unable to surmount many operational challenges such as limited network lifetime, which strangle their widespread deployment. To prolong WSN lifetime, most of the existing clustering schemes are geared towards homogeneous WSN. This paper presents enhanced developed distributed energy-efficient clustering (EDDEEC) scheme for heterogeneous WSN. EDDEEC mainly consists of three constituents i.e., heterogeneous network model, energy consumption model, and clustering-based routing mechanism. Our heterogeneous network model is based on three energy levels of nodes. Unlike most works, our energy consumption model takes into account the impact of radio environment. Finally, the proposed clustering mechanism of EDDEEC changes the cluster head selection probability in an efficient and dynamic manner. Simulation results validate and confirm the performance supremacy of EDDEEC compared to existing schemes in terms of various metrics such as network life. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is the latest Internet development, with billions of Internet-connected devices and a wide range of industrial applications. Wireless sensor networks are an important part of the Internet of Things. It has received extensive attention from researchers due to its large-scale, self-organizing, and dynamic characteristics and has been widely used in industry, traffic information, military, environmental monitoring, and so on. With the development of microprocessor technology, sensor nodes are becoming more and more powerful, which enables the same wireless sensor networks (WSNs) platform to meet the different quality of service (QoS) requirements of many applications. Applications for industrial wireless sensor networks range from lower physical layers to higher application layers. The same wireless sensor network sometimes needs to process information from different layers. Traditional protocols lack differentiated services and cannot make full use of network resources. In this paper, an Adaptive Retransmit Mechanism for Delay Differentiated Services (ARM-DDS) scheme is proposed to meet different levels of delays of applications. Firstly, we analyze the impact of different retransmit mechanisms and parameter optimization on delays and energy consumption. Based on the results of the analysis, in ARM-DDS scheme, for routes with transmission delay tolerance, energy-saving retransmission mechanisms are used, and low-latency retransmission mechanisms are used for latency-sensitive routes. In this way, the data routing delays of different applications are guaranteed within bound and the energy consumption of the network is reduced. What is more, ARM-DDS scheme makes full use of the residual energy of the network and uses a small delay routing retransmit mechanism in the far-sink area to reduce end-to-end delay. Both theoretical analysis and simulation experiments show that under the premise of the same reliability requirements, ARM-DDS scheme reduces data transmission delay 12.1% and improves network energy utilization 28%. Given that the reliability requirements of the data stream are different, the scheme can also extend the network lifetime. [ABSTRACT FROM AUTHOR]

As a large number of small base stations have been deployed in dense heterogeneous networks, the increasing cost of energy has become a challenging issue. In this paper, we propose an optimized clustering-based sleep strategy to release the power consumption and interference in the system. First, we group the small base stations with large interference as a cluster in which the binary particle swarm optimization (BPSO) algorithm is used to formulate a sleep strategy for small base stations in the clusters. Since the small base stations between clusters have less interference, the whole spectrum resources can be shared in all clusters and the orthogonal spectrum resources are allocated for the small base stations in the same cluster. The strategy effectively restrains the co-tier interference in the system. Furthermore, a separate sleep strategy is applied for each cluster to improve the sleep and activation efficiency of the small base stations. The simulation results show that the proposed sleep strategy can improve the satisfaction of user equipment and reduce the power consumption. As the increasing number of user equipment, the outage probability of the system can be reduced effectively. [ABSTRACT FROM AUTHOR]

In wireless sensor networks, sensor nodes are usually powered by battery and thus have very limited energy. Saving energy is an important goal in designing a WSN. It is known that clustering is an effective method to prolong network lifetime. Due to the development of big data, there are more sensor nodes and data needed to process. So how to cluster sensor nodes cooperatively and achieve an optimal number of clusters in a big data WSN is an open issue. In this paper, we first propose an analytical model to give the optimal number of clusters in a wireless sensor network. We then propose a centralized cluster algorithm based on spectral partitioning method. After that, we present a distributed implementation of the clustering algorithm based on fuzzy C-means method. Finally, we conduct extensive simulations, and the results show that the proposed algorithms outperform the hybrid energy-efficient distributed (HEED) clustering algorithm in terms of energy cost and network lifetime. [ABSTRACT FROM AUTHOR]

In mobile ad hoc networks (MANETs), the reliability of nodes, quality of data and access control cannot be achieved successfully for various network functionalities through traditional cryptographic security, which makes MANET vulnerable to illegitimate node behaviour changes. These node misbehaviours, referred as soft security threats, need to be detected and prevented in order to protect against the accumulation of false measurements with selfish and malicious intentions. Trust has been employed as a powerful tool to handle the soft security threats and to provide security among uncertain and dynamic nodes effectively in MANET. Therefore, it is of great importance that efficient trust management mechanisms should be developed in a public key infrastructure (PKI), in order to verify the identities on the ad hoc networks for reliable and secure group communication. However, the independent nature of nodes and the computational complexities make the trust management a challenging one in MANET. In this paper, we present an efficient distributed trust computation and misbehaviour verification method with Bayesian and Evidence theorem, on hexagonally clustered MANET. Besides, a secured PKI system is designed in the paper by applying the proposed trust management scheme in terms of certificate revocation, which is an important functionality of PKI cryptosystem. The uncertainty impacts the node’s anticipation of neighbour’s behaviour and decisions during communication; we include uncertainty in the trust management system. An efficient method to reduce the uncertainty is to exploit the mobility characteristics of MANET that accelerates the trust convergence. The simulation results reveal a better performance against adversaries in creating considerable untrustworthy transactions with a mobility-aware cluster guarantee. Moreover, the proposed trust application shows its betterment in the revocation process in terms of revocation rate and time. Thus, the proposed scheme provides an effective security solution that incorporates the optimistic features of trust mechanisms and hierarchical Voronoi clustering. [ABSTRACT FROM AUTHOR]

Road traffic information can be utilized in many applications of intelligent transport systems. It can be collected from vehicles and sent over a vehicular ad hoc network (VANET) to roadside units (RSUs) acting as sink nodes. Due to rapid mobility and limited channel capacity in a VANET where vehicles must compete to access the RSUs to report their data, clustering is used to create a group of vehicles to facilitate data transfer to the RSUs. Unlike previous works that focus on cluster lifetime or throughput, we formulate a coalitional graph game for multi-hop clustering (CGG-MC) model to create a multi-hop cluster with the largest possible coverage area for a given transmission delay time constraint to economize on the number of RSUs installed. Vehicles cooperatively form a proper coalition with relation directed graphs among vehicles in a multi-hop cluster to collect, aggregate, and forward data to RSUs instead of individually competing to connect directly to RSUs. Vehicles decide to join or leave the coalition based on their individual utility, which is a weighted function of the coverage area, number of members in the cluster, relative velocities, distance to sink nodes, and transmission delay toward the sink nodes. The distributed-solution approach based on probabilistic greedy merging of coalitions is used to derive the grand coalition, and the probability of grand coalition formation is analyzed by using a discrete-time Markov chain. Our results show that the proposed solution approach yields a 95 % confidence interval of the average utility between 61 and 68 % relative to the maximum utility in the centralized-solutions. Additionally, our CGG-MC model outperforms the non-cooperation model by approximately 166 % in terms of enlarging the coverage network area under a transmission delay time constraint. [ABSTRACT FROM AUTHOR]

Wireless sensor network (WSN)-based Internet of Things (IoT) applications suffer from issues including limited battery capacity, frequent disconnections due to multi-hop communication and a shorter transmission range. Clustering and routing are treated separately in different solutions and, therefore, efficient solutions in terms of energy consumption and network lifetime could not be provided. This work focuses data collection from IoT-nodes distributed in an area and connected through WSN. We address two interlinked issues, clustering and routing, for large-scale IoT-based WSN and propose an improved clustering and routing protocol to jointly solve both of these issues. Improved clustering and routing provide area-based clustering derived from the transmission range of network nodes. During process of clustering, cluster-heads are selected in such a way that provide fail-over-proof routing. An efficient routing path is achieved by finding the minimal hop-count with the availability of alternate routing paths. The results are compared with state-of-the-art benchmark protocols. Theoretical and simulation results demonstrate reliable network topology, improved network lifetime, efficient node density management and improved overall network capacity. [ABSTRACT FROM AUTHOR]

Clustering, a traditional machine learning method, plays a significant role in data analysis. Most clustering algorithms depend on a predetermined exact number of clusters, whereas, in practice, clusters are usually unpredictable. Although the Elbow method is one of the most commonly used methods to discriminate the optimal cluster number, the discriminant of the number of clusters depends on the manual identification of the elbow points on the visualization curve. Thus, experienced analysts cannot clearly identify the elbow point from the plotted curve when the plotted curve is fairly smooth. To solve this problem, a new elbow point discriminant method is proposed to yield a statistical metric that estimates an optimal cluster number when clustering on a dataset. First, the average degree of distortion obtained by the Elbow method is normalized to the range of 0 to 10. Second, the normalized results are used to calculate the cosine of intersection angles between elbow points. Third, this calculated cosine of intersection angles and the arccosine theorem are used to compute the intersection angles between elbow points. Finally, the index of the above-computed minimal intersection angles between elbow points is used as the estimated potential optimal cluster number. The experimental results based on simulated datasets and a well-known public dataset (Iris Dataset) demonstrated that the estimated optimal cluster number obtained by our newly proposed method is better than the widely used Silhouette method. [ABSTRACT FROM AUTHOR]

Data mining technology has been applied in many fields. Prototype-based cluster analysis is an important data mining method, but its ability to discover knowledge is limited because of the need to know the number of target data categories and cluster prototypes in advance. Artificial immune evolutionary network clustering is a clustering method based on network structure. Compared with prototype-based cluster analysis, it has the advantage of realizing unsupervised learning and clustering without any prior knowledge of data. However, artificial immune evolutionary network clustering also has problems such as a lack of guidance in the clustering process, fuzzy boundary sensitivity, and difficulty in determining parameters. To solve these problems, an artificial immune network clustering algorithm based on a cultural algorithm is proposed. First, three kinds of knowledge are constructed: normative knowledge is used to regulate the spatial range of population initialization to avoid blindness; state knowledge is used to distinguish the type of antigen, and immune defense measures are taken to prevent the network structure caused by noise and boundaries from being unclear; and topology knowledge is used to guide the antigen for optimal antibody search. Second, topology knowledge in the cultural algorithm is used to characterize the distribution of antigens and antibodies in space, and elite learning is used to improve the traditional clone mutation operator. Based on the shadow set theory, a method for adaptively determining the compression threshold is proposed. Finally, the results of simulation experiments show that the proposed algorithm can effectively overcome the above problems, and the clustering performances on a synthetic dataset and an actual dataset are satisfactory. [ABSTRACT FROM AUTHOR]

Reservoir computing (RC) is considered as a suitable alternative for descending gradient methods in recursive neural networks (RNNs) training. The echo state network (ESN) is a platform for RC and nonlinear system simulation in the cloud environment with many external users. In the past researches, the highest eigenvalue of reservoir connection weight (spectral radius) was used to predict reservoir dynamics. Some researchers have illustrated; the characteristics of scale-free and small-world can improve the approximation capability in echo state networks; however, recent studies have shown importance of the infrastructures such as clusters and the stability criteria of these reservoirs as altered. In this research, we suggest a high clustered ESN called HCESN that its internal neurons are interconnected in form of clusters. Each of the clusters contains one backbone and a number of local nodes. We implemented a classical clustering algorithm, called K-means, and three optimization algorithms including genetic algorithm (GA), differential evolution (DE), and particle swarm optimization (PSO) to improve the clustering efficiency of the new reservoir and compared them with each other. For investigating the spectral radius and predictive power of the resulting reservoirs, we also applied them to the laser time series and the Mackey-Glass dynamical system. It is demonstrated that new clustered reservoirs have some specifications of biologic neural systems and complex networks like average short path length, high clustering coefficient, and power-law distribution. The empirical results illustrated that the ESN based on PSO could strikingly enhance echo state property (ESP) and obtains less chaotic time series prediction error compared with other works and the original version of ESN. Therefore, it can approximate nonlinear dynamical systems and predict the chaotic time series. [ABSTRACT FROM AUTHOR]

Ina wireless ad hoc network, single-antenna nodes can cooperate in their transmissions to create a virtual multiple-input multiple-output (MIMO) channel for maximizing the spatial multiplexing gain. However, implementing virtual-MIMO transmission can result in overwhelming overhead, such as channel state information (CSI) feedback and local data exchange, as the number of cooperating nodes increases. To suppress this overhead, we apply the principle of virtual-MIMO transmission to CSI feedback and design a novel framework for supporting parallel feedback streams, called virtual-MIMO feedback. Building on this feedback framework, we optimize virtual-MIMO transmission techniques and quantify their throughput gains in the presence of CSI and local-exchange overhead. To be specific, the outage capacities of these virtual-MIMO techniques are analyzed, and the corresponding bandwidth allocation for local data exchange and long-range transmission is optimized in a way to maximize overall spectral efficiency. Finally, the improvement of spectral efficiency in proposed virtual-MIMO feedback and transmission is demonstrated by the simulation results. [ABSTRACT FROM AUTHOR]