1,349 research outputs found
A Survey and Future Directions on Clustering: From WSNs to IoT and Modern Networking Paradigms
Many Internet of Things (IoT) networks are created as an overlay over traditional ad-hoc networks such as Zigbee. Moreover, IoT networks can resemble ad-hoc networks over networks that support device-to-device (D2D) communication, e.g., D2D-enabled cellular networks and WiFi-Direct. In these ad-hoc types of IoT networks, efficient topology management is a crucial requirement, and in particular in massive scale deployments. Traditionally, clustering has been recognized as a common approach for topology management in ad-hoc networks, e.g., in Wireless Sensor Networks (WSNs). Topology management in WSNs and ad-hoc IoT networks has many design commonalities as both need to transfer data to the destination hop by hop. Thus, WSN clustering techniques can presumably be applied for topology management in ad-hoc IoT networks. This requires a comprehensive study on WSN clustering techniques and investigating their applicability to ad-hoc IoT networks. In this article, we conduct a survey of this field based on the objectives for clustering, such as reducing energy consumption and load balancing, as well as the network properties relevant for efficient clustering in IoT, such as network heterogeneity and mobility. Beyond that, we investigate the advantages and challenges of clustering when IoT is integrated with modern computing and communication technologies such as Blockchain, Fog/Edge computing, and 5G. This survey provides useful insights into research on IoT clustering, allows broader understanding of its design challenges for IoT networks, and sheds light on its future applications in modern technologies integrated with IoT.acceptedVersio
Mobility management in 5G heterogeneous networks
In recent years, mobile data traffic has increased exponentially as a result of widespread popularity and uptake of portable devices, such as smartphones, tablets and laptops. This growth has placed enormous stress on network service providers who are committed to offering the best quality of service to consumer groups. Consequently, telecommunication engineers are investigating innovative solutions to accommodate the additional load offered by growing numbers of mobile users.
The fifth generation (5G) of wireless communication standard is expected to provide numerous innovative solutions to meet the growing demand of consumer groups. Accordingly the ultimate goal is to achieve several key technological milestones including up to 1000 times higher wireless area capacity and a significant cut in power consumption.
Massive deployment of small cells is likely to be a key innovation in 5G, which enables frequent frequency reuse and higher data rates. Small cells, however, present a major challenge for nodes moving at vehicular speeds. This is because the smaller coverage areas of small cells result in frequent handover, which leads to lower throughput and longer delay.
In this thesis, a new mobility management technique is introduced that reduces the number of handovers in a 5G heterogeneous network. This research also investigates techniques to accommodate low latency applications in nodes moving at vehicular speeds
Routing Optimizing Decisions in MANET: The Enhanced Hybrid Routing Protocol (EHRP) with Adaptive Routing based on Network Situation
Mobile ad hoc networks (MANETs) are wireless networks that operate without a fixed infrastructure or base station. In MANETs, each node acts as a data source and a router, establishing connections with its neighboring nodes to facilitate communication. This research has introduced the Enhanced Hybrid Routing Protocol (EHRP), which combines the OLSR, AOMDV, and AODV routing protocols while considering the network situation for improved performance. The EHRP protocol begins by broadcasting a RREP (Route Reply) packet to discover a route. The selection of routing options is based on the current network situation. To determine the distance between the source and destination nodes, the proposed EHRP initiates a RREQ (Route Request) packet. In situations where network mobility exceeds the capabilities of the AODV protocol, the EHRP protocol can utilize the OLSR routing protocol for route selection and data transmission, provided that at least 70% of the network nodes remain stable. Additionally, the EHRP protocol effectively handles network load and congestion control through the utilization of the AOMDV routing protocol. Compared to the hybrid routing protocol, the enhanced hybrid routing protocol (EHRP) demonstrates superior performance. Its incorporation of the OLSR, AOMDV, and AODV protocols, along with its adaptive routing adaptation based on network conditions, allows for efficient network management and improved overall network performance.
The analysis of packet delivery ratio for EHRP and ZRP reveals that EHRP achieves a packet delivery ratio of 98.01%, while ZRP achieves a packet delivery ratio of 89.99%. These results indicate that the enhanced hybrid routing protocol (EHRP) outperforms the hybrid routing protocol (ZRP) in terms of packet delivery ratio. EHRP demonstrates a higher level of success in delivering packets to their intended destinations compared to ZRP.
The analysis of normal routing load for EHRP and ZRP reveals that EHRP exhibits a normal routing load of 0.13%, while ZRP exhibits a higher normal routing load of 0.50%. Based on these results, it can be concluded that the performance of the Enhanced Hybrid Routing Protocol (EHRP) is significantly better than that of the Hybrid Routing Protocol (ZRP) when considering the normal routing load. EHRP demonstrates a lower level of routing overhead and more efficient resource utilization compared to ZRP in scenarios with normal routing load.
When comparing the average end-to-end delay between the Enhanced Hybrid Routing Protocol (EHRP) and ZRP, the analysis reveals that EHRP achieves an average delay of 0.06, while ZRP exhibits a higher average delay of 0.23. These findings indicate that the Enhanced Hybrid Routing Protocol (EHRP) performs better than ZRP in terms of average end-to-end delay. EHRP exhibits lower delay, resulting in faster and more efficient transmission of data packets from source to destination compared to ZRP.
After considering the overall parameter matrix, which includes factors such as normal routing load, data send and receive throughput, packet delivery ratio, and average end-to-end delay, it becomes evident that the performance of the Enhanced Hybrid Routing Protocol (EHRP) surpasses that of the current hybrid routing protocol (ZRP). Across these metrics, EHRP consistently outperforms ZRP, demonstrating superior performance and efficiency. The Enhanced Hybrid Routing Protocol (EHRP) exhibits better results in terms of normal routing load, higher throughput for data transmission and reception, improved packet delivery ratio, and lower average end-to-end delay. Overall, EHRP offers enhanced performance and effectiveness compared to the existing hybrid routing protocol (ZRP)
Advancement in infotainment system in automotive sector with vehicular cloud network and current state of art
The automotive industry has been incorporating various technological advancement on top-end versions of the vehicle order to improvise the degree of comfortability as well as enhancing the safer driving system. Infotainment system is one such pivotal system which not only makes the vehicle smart but also offers abundance of information as well as entertainment to the driver and passenger. The capability to offer extensive relay of service through infotainment system is highly dependent on vehicular adhoc network as well as back end support of cloud environment. However, it is know that such legacy system of vehicular adhoc network is also characterized by various problems associated with channel capacity, latency, heterogeneous network processing, and many more. Therefore, this paper offers a comprehensive insight to the research work being carried out towards leveraging the infotainment system in order to obtain the true picture of strength, limitation, and open end problems associated with infotainment system
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Cognitive virtual ad hoc mobile cloud-based networking architecture
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonThis thesis proposed cognitive techniques and intelligent algorithms that offered adaptive and advanced facilities to cloud-based networking by using Virtual Ad Hoc Mobile Cloud Computing Networks architecture (VAMCCNs). This is presented as a working case to address their global network challenges and to add cognitive support to the network design and implementation for better meeting traffic management and application requirements in mission objectives. The thesis concentrates on three main contributions.
Firstly, an adaptive model, namely: a Heterogeneous Mobile Cloud Computing Network (HMCCN), was proposed to integrate different cloud networks architectures into one workflow. The cognitive data offloading task and the routing decision methods were applied using two different approaches: Fuzzy Analytic Hierarchy system (FAH) as a first approach and cognitive Software Defined Network (SDN) model as a second centralised approach. Experimental results show improvement in network reliability and throughputs, minimised in both nodes’ energy consumption and network latency with efficient intelligent data load balance and network resources allocation with best cloud model selection.
Secondly, based on a virtual Ad Hoc cloud network with a realistic Random Waypoint Motion (RWM) model, an innovative cognitive routing algorithm was presented to improve efficient and reliable route selection among multiple possible routes. Routing protocols based on conventional, Fuzzy logic used important parameters with two data collections and decisions techniques and a new adaptive Intelligent Hybrid Fuzzy-Neural routing protocol (IHFN) that included prior knowledge to the network of the underlying motion and energy parameters were all proposed and compared. Results with the new hybrid algorithm shown a significant improvement to solve the network end-to-end performance degradation problem. The new hybrid protocol improved network throughput with an average of 20% higher than traditional Ad Hoc On-Demand Distance Vector (AODV) Routing protocol, improved the usage of network resources and reduced the maintenance process in adynamic topologies network.
Finally, based on datasets collected from a realistic motion RWM model in a virtual Ad Hoc cloud network, the performance behaviour of six selected deep learning algorithms to predict the next steps of positions, speed and residual battery energy values of these mobile nodes have been evaluated and compared. This work goes further by presenting two algorithm's training techniques to predict the next 300-time steps of position, speed, and energy. Results and dissuasion show the differences concerning prediction accuracy between using the single node dataset model or Multiple node's dataset model
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