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We propose a context-awareness routing algorithm—the DDV- (Dynamic Direction Vector-) hop algorithm—in mobile ad hoc networks. The existing algorithm in MANET has the limitations of the dynamic network topology and the absence of network expandability of the mobility of nodes. The proposed algorithm performs cluster formation for the base station u...
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Citations
... WCA is further developed by changing the elements of WCA and improved the efficiency with minimum calculation and transmission expenses for MANET [15]. The direction limit and threshold speed of UAVs based clustering is performed in DDV (Dynamic Direction Vector) technique to analyze the performance based on packet delivery ratio versus network length, speed and transmission ranges of MANET [23]. K-Means and Floyd-Warshall are combined for utilizing the dynamic routing and clustering in Vehicular Adhoc Network (VANET) to expand the permanence time and range of network. ...
Unmanned Aerial Vehicles (UAVs) are well appropriate devices for wireless communication in Flying Ad-hoc Networks deployed in several applications like disaster and rescue management. The challenging issues of UAVs such as tiny flight time and infertile routing in view of constrained battery ability and maximum movement are abridged by utilizing the Salp Swarm Optimization based Clustering Algorithm (SSOCA). The proficiency of SSOCA is analyzed in terms of the packet delivery ratio, cluster existence period, total clusters, throughput, delay, cluster construction time, and energy consumption and consequences clarify the preferable adeptness of SSOCA against several previous clustering approaches such as MOPSO, CLPSO, CACONET, and CAVDO.
... As breakdowns in connection relations often happen in VANETs, guaranteeing the stability of communication is extra difficult in VANETs as compared to normal MANETs. An actual and low-cost solution to decrease the mobility effect and develop the VANET network connectivity involves forming a clustering with hierarchical structure inside the network [1][2][3][4][5][6][7][8][9][10][11][12]. The clustering procedure is the separation of networks into small groups. ...
... Many parameters affect the clustering, such as the distance between nodes, capability of link communication, and the improvement of a comprehensive network presentation. Small clusters can work more efficiently [3][4][5][6][7][8][9]. The ground network vehicles are divided into simulated groups recognized as clusters through the clustering method. ...
... The latter can offer an active solution for the abovementioned problems. A vehicle is select to manage the connection among its Cluster Members (CM), called a Cluster Head (CH) an interacts with other sheets of a mutual network [2][3][4][5][6][7][8][9][10][11]. ...
VANET ("vehicular ad hoc network") is a type of networks that consist of many vehicles acting as moving nodes. They are connected with other vehicles through an ad hoc wireless network so as to increase traffic security and provide relaxation to road users. Sending the messages to the final destination in VANETs is a challenging mission because of its relatively high mobility and dynamism. The clustering technique addresses such issues, as it gathers vehicles based upon several predefined metrics such us density, speed, and physical vehicles locations. Clustering in VANET is one of the controller techniques for dynamic form. In this paper, a new method is presented for clustering that suits the VANET environment with the purpose of improving the network cluster stability. This method takes a number of parameters into consideration, such as the coverage area and speed to make the cluster structure comparatively stable. In addition, and advance is obtained in tems of the cluster-head selections algorithm, and the data exchange is improved through clusters.
... In recent years, many scholars at home and abroad have carried out in-depth and detailed research work on routing protocols. A lot of research work has shown that routing protocols can be divided into two different types, that is, plane routing and hierarchical routing [23,24]. In the plane routing protocol, all sensor nodes have the same status and functions, and the nodes complete the data collection task, data transmission, and routing process through collaborative computing. ...
In order to better improve the reliability of data transmission and extend the network lifetime, the paper proposes the Sensing Clustering Routing Algorithm Based on Collaborative Computing (SCR-CC). The proposed algorithm uses the characteristics of the perceptual radius, which obey the normal distribution, and gives the process of completing the expected value of the data transmission of any two nodes in the cluster. Secondly, the paper analysed the necessary conditions of the existence for the expected value of the number of neighbour nodes when the redundant nodes are closed and the working nodes meet arbitrary differences. Thirdly, the cluster angle formed by the base station and the cluster is used to optimize the clustering structure and complete the dynamic clustering process to achieve the energy balance of the entire network. Finally, the simulation experiments show that the proposed SCR-CC algorithm compared with the other three algorithms reduces the number of failed nodes by 11.37% on average and increases the network lifetime by 27.09% on average; therefore, the efficiency and effectiveness of the SCR-CC algorithm are verified.
... The elected CH is found to be more realistic and more energy-aware. The research works in [7][8][9][10][11][12][13][14][15][16][17][18][19][20] discuss many potential clustering techniques to make the sensor networks energy efficient. ...
... Thus, Basu et al. 6 proposed to use the ratio of the signal power levels of successive transmissions from each neighbor node to detect any movement. Based on this metric, they proposed a clustering algorithm called MOBIC, and with the same respect, many studies 6,7,9,25,26 1950005-3 ...
Network clustering is the process of partitioning a network into a number of virtual entities mastered by certain nodes, called cluster centers that are responsible for collecting and maintaining topology information and managing the routing processes. In ad hoc networking, clustering has been introduced to deal with the dynamic topology by providing a temporarily stable network core. Clustering process mainly depends on the metric upon which the selection of cluster centers is performed. A wide range of clustering metrics were introduced in the literature based on network issues including mobility and connectivity degree, giving rise to a variety of clustering schemes. Although clustering provides energy consumption reduction, residual energy has not received enough attention and few studies have addressed the clustering on the basis of this feature. This paper discusses the current clustering metrics and proposes an energy-degree evaluation metric with mobility consideration taking into account the nodes residual energy and the network connectivity as two main keys of clustering.
... In addition, clustering is also a good way to solve the capacity and scalability problems to reduce data transmission collisions. 8,9 For example, low-energy adaptive clustering hierarchy (LEACH), 10 is one of famous clustering protocols, in which CH is elected randomly and periodically. In the existing works, although CH is in active state, it cannot detect a target appeared on the edge of the cluster since CH always is located at the cluster centre. ...
Due to energy limitation in wireless sensor networks, clustering is an efficient scheme which has been widely used in building practical wireless sensor networks, and various cluster head selection methods have been proposed nowadays. However, less emphasis was placed on the application constraints cluster head selection. In traditional clustering wireless sensor networks, cluster head is always located at the cluster centre and cannot detect an intruding target since the target first transits the border. Moreover, the data sensed from a target are sent by each cluster head through different routings to the sink so that it cannot be aggregated efficiently near the data source. In order to address these problems, this article proposes an efficient target tracking approach, in which the nodes on the edge of a cluster instead of the centred nodes are chosen as cluster heads so that cluster heads can serve as manager and monitoring node. Furthermore, we choose a collecting cluster head to collect the sensed data from the cluster heads around the target to facilitate data aggregation. Hence, the sensed data can be aggregated near to the data source, which avoids the data long-distance transmission and reduces data gathering costs. Moreover, each cluster head has different lifetime in the efficient target tracking approach according to its location and residual energy to balance the energy cost. Experimental results show that efficient target tracking approach outperformed the state-of-the-art approaches by improving the energy consumption as well as prolonging the network lifetime by about 20% as the 20% nodes die.
... Ad-hoc network is a repeatedly jumping, nothing central, and self-organizing network [1][2]. the entire network has not stable infrastructure, and each node is mobile. ...
... The property of mobility in mobile ad-hoc network has also an important impact on the stability of the clusters. So, taking into consideration the factor of mobility during the construction of the clusters structure, will have a significant impact on the stability of the clusters and thus other parameters of the network [22]. This technique tends to group the different node that has the same mobility model into the same cluster, in order to reduce the operation of the re-clustering also the re-affiliation of the cluster-member in the clusters. ...
Mobile ad hoc network become nowadays more and more used in different domains, due to its flexibility and low cost of deployment. However, this kind of network still suffering from several problems as the lack of resources. Many solutions are proposed to face these problems, among these solutions there is the clustering approach. This approach tries to partition the network into a virtual group. It is considered as a primordial solution that aims to enhance the performance of the total network, and makes it possible to guarantee basic levels of system performance. In this paper, we study some schemes of clustering such as Dominating-Set-based clustering, Energy-efficient clustering, Low-maintenance clustering, Load-balancing clustering, and Combined-metrics based clustering.
... Table 1 shows a summary scheme of the analyzed protocols. Others related works are [15][16][17]. ...
... In [16], the DDV-hop algorithm is proposed. This is an algorithm is a cluster algorithm that uses direction and velocity as the components of cluster formation. ...
In this work, an ACO routing protocol for mobile ad hoc networks based on AntHocNet is specified. As its predecessor, this new protocol, called AntOR, is hybrid in the sense that it contains elements from both reactive and proactive routing. Specifically, it combines a reactive route setup process with a proactive route maintenance and improvement process. Key aspects of the AntOR protocol are the disjoint-link and disjoint-node routes, separation between the regular pheromone and the virtual pheromone in the diffusion process and the exploration of routes, taking into consideration the number of hops in the best routes. In this work, a family of ACO routing protocols based on AntOR is also specified. These protocols are based on protocol successive refinements. In this work, we also present a parallelized version of AntOR that we call PAntOR. Using programming multiprocessor architectures based on the shared memory protocol, PAntOR allows running tasks in parallel using threads. This parallelization is applicable in the route setup phase, route local repair process and link failure notification. In addition, a variant of PAntOR that consists of having more than one interface, which we call PAntOR-MI (PAntOR-Multiple Interface), is specified. This approach parallelizes the sending of broadcast messages by interface through threads.
... The proposed algorithm uses an energy consumption equation of the first-order ratio model. [25][26][27][28][29][30][31] The equation is given as ...
Mobile ad hoc networks comprise mobile nodes. The nodes both send and receive messages and can communicate with each other. Thus, the network builds its own network structure that is not dependent on the infrastructure. Owing to the characteristics of mobile ad hoc networks, they have been used in environments of poor communication, such as those in which the infrastructure cannot be built; for example, disaster areas and war zones. In this article, we propose an advanced energy-conserving optimal path schedule algorithm. The proposed algorithm sets the routing path using the relative angle, which is the distance between the source node and the base station. Using simulation results, we compared the proposed algorithm to existing algorithms. The protocol used by the proposed algorithm provides a higher packet delivery ratio and lower energy consumption than the lowest ID clustering algorithm and the mobility-based metric for clustering in the mobile ad hoc network algorithm.
