Communications and Network
Scientific Research Publishing
About: Communications and Network is an academic journal published by Scientific Research Publishing. The journal publishes majorly in the area(s): Wireless network & Wireless sensor network. It has an ISSN identifier of 1947-3826. It is also open access. Over the lifetime, 459 publications have been published receiving 2672 citations. The journal is also known as: Communications and network.
Topics: Wireless network, Wireless sensor network, Communication channel, Quality of service, Routing protocol
TL;DR: Various possible applications of vehicular network, along with its features, and implementations in the real world are primarily categorized.
Abstract: The Vehicular Safety Consortium (VSC), the Crash-Avoidance Metrics Partnership (CAMP) consortium and the Vehicle Infrastructure Initiative (VII)  along with the giants of the light-duty vehicle manufactures, are working to develop pre-competitive safety technologies and various applications that can be offered in Vehicular ad-hoc Networks (VANETs), a special kind mobile ad-hoc networks where wireless equipped element called on-board unit (OBU) in vehicles form a network with the Roadside unit (RSU) without any additional infrastructure. In this paper, we are primarily categorizing various possible applications of vehicular network, along with its features, and implementations in the real world.
TL;DR: The paper proposes an improved RSSI-based algorithm, the experimental results show that compared with original RSSi-based localization algorithms the algorithm improves the localization accuracy and reduces the deviation.
Abstract: Wireless node localization is one of the key technologies for wireless sensor networks. Outdoor localization can use GPS, AGPS (Assisted Global Positioning System) , but in buildings like supermarkets and underground parking, the accuracy of GPS and even AGPS will be greatly reduced. Since Indoor localization requests higher accuracy, using GPS or AGPS for indoor localization is not feasible in the current view. RSSI-based trilateral localization algorithm, due to its low cost, no additional hardware support, and easy-understanding, it becomes the mainstream localization algorithm in wireless sensor networks. With the development of wireless sensor networks and smart devices, the number of WIFI access point in these buildings is increasing, as long as a mobile smart device can detect three or three more known WIFI hotspots’ positions, it would be relatively easy to realize self-localization (Usually WIFI access points locations are fixed). The key problem is that the RSSI value is relatively vulnerable to the influence of the physical environment, causing large calculation error in RSSI-based localization algorithm. The paper proposes an improved RSSI-based algorithm, the experimental results show that compared with original RSSI-based localization algorithms the algorithm improves the localization accuracy and reduces the deviation.
TL;DR: This research proposes implementing artificial neural networks to optimize the job scheduling results in cloud as it can find new set of classifications not only search within the available set.
Abstract: Cloud computing aims to maximize the benefit of distributed resources and aggregate them to achieve higher throughput to solve large scale computation problems. In this technology, the customers rent the resources and only pay per use. Job scheduling is one of the biggest issues in cloud computing. Scheduling of users’ requests means how to allocate resources to these requests to finish the tasks in minimum time. The main task of job scheduling system is to find the best resources for user’s jobs, taking into consideration some statistics and dynamic parameters restrictions of users’ jobs. In this research, we introduce cloud computing, genetic algorithm and artificial neural networks, and then review the literature of cloud job scheduling. Many researchers in the literature tried to solve the cloud job scheduling using different techniques. Most of them use artificial intelligence techniques such as genetic algorithm and ant colony to solve the problem of job scheduling and to find the optimal distribution of resources. Unfortunately, there are still some problems in this research area. Therefore, we propose implementing artificial neural networks to optimize the job scheduling results in cloud as it can find new set of classifications not only search within the available set.
TL;DR: Clustering in mobile ad hoc networks plays a vital role in improving resource management and network performance (routing delay, bandwidth consumption and throughput) and this paper presents a study and analyze of some existing clustering approaches for MANETs that recently appeared in literature.
Abstract: Mobile ad-hoc networks (MANETs) are a specific kind of wireless networks that can be quickly deployed without pre- existing infrastructures. They are used in different contexts such as collaborative, medical, military or embedded applications. However, MANETs raise new challenges when they are used in large scale network that contain a large number of nodes. Subsequently, many clustering algorithms have emerged. In fact, these clustering algorithms allow the structuring of the network into groups of entities called clusters creating a hierarchical structure. Each cluster contains a particular node called cluster head elected as cluster head according to a specific metric or a combination of metrics such as identity, degree, mobility, weight, density, etc. MANETs has drawbacks due to both the characteristics of the transmission medium (transmission medium sharing, low bandwidth, etc.) and the routing protocols (information diffusion, path finding, etc.). Clustering in mobile ad hoc networks plays a vital role in improving resource management and network performance (routing delay, bandwidth consumption and throughput). In this paper, we present a study and analyze of some existing clustering approaches for MANETs that recently appeared in literature, which we classify as: Identifier Neighbor based clustering, Topology based clustering, Mobility based clustering, Energy based clustering, and Weight based clustering. We also include clustering definition, review existing clustering approaches, evaluate their performance and cost, discuss their advantages, disadvantages, features and suggest a best clustering approach.
TL;DR: A genetic algorithm is presented that searches for an optimal or near optimal solution to the coverage holes problem in hybrid wireless sensor networks and can optimize the network coverage in terms of the overall coverage ratio and the number of additional mobile nodes.
Abstract: In hybrid wireless sensor networks composed of both static and mobile sensor nodes, the random deployment of stationary nodes may cause coverage holes in the sensing field. Hence, mobile sensor nodes are added after the initial deployment to overcome the coverage holes problem. To achieve optimal coverage, an efficient algorithm should be employed to find the best positions of the additional mobile nodes. This paper presents a genetic algorithm that searches for an optimal or near optimal solution to the coverage holes problem. The proposed algorithm determines the minimum number and the best locations of the mobile nodes that need to be added after the initial deployment of the stationary nodes. The performance of the genetic algorithm was evaluated using several metrics, and the simulation results demonstrated that the proposed algorithm can optimize the network coverage in terms of the overall coverage ratio and the number of additional mobile nodes.