Network Path Optimization Strategy using Collaborative Cache for Delay Tolerant Networks

The data transmissions over Delay Tolerant Networks (DTN) and Social-based Opportunistic networks have increased in the last few years due to a higher demand for remote transmissions. The wide applications of DTN have significantly motivated the researchers to focus on finding optimized routing strategies by optimizing various parameters such as energy, cost and congestion. Nonetheless, these parallel research outcomes have reported few further bottlenecks to improve the strategies. Henceforth, this work proposes a novel approach to optimize the routing paths using the cache collaboration method. This proposed method identifies the data-sharing strategies and subsequently identifies the sub-set of the paths between the source and destinations. Further optimizes the path using standard measures such as cost, transmission speed, and the network traffic conditions; lastly Data-Centric and Cost Optimized Routing Path is Identification. This work results in a nearly 20% reduction in the distance between the nodes, a 15% reduction of time in path identification and a nearly 50% reduction in cache allocation demand over multiple iterations compared to the existing models

The cloud-to-edge-to-IoT continuum as an enabler for search and rescue operations

When a natural or human disaster occurs, time is critical and often of vital importance. Data from the incident area containing the information to guide search and rescue (SAR) operations and improve intervention effectiveness should be collected as quickly as possible and with the highest accuracy possible. Nowadays, rescuers are assisted by different robots able to fly, climb or crawl, and with different sensors and wireless communication means. However, the heterogeneity of devices and data together with the strong low-delay requirements cause these technologies not yet to be used at their highest potential. Cloud and Edge technologies have shown the capability to offer support to the Internet of Things (IoT), complementing it with additional resources and functionalities. Nonetheless, building a continuum from the IoT to the edge and to the cloud is still an open challenge. SAR operations would benefit strongly from such a continuum. Distributed applications and advanced resource orchestration solutions over the continuum in combination with proper software stacks reaching out to the edge of the network may enhance the response time and effective intervention for SAR operation. The challenges for SAR operations, the technologies, and solutions for the cloud-to-edge-to-IoT continuum will be discussed in this paper

Design and Analysis of Beamforming in mmWave Networks

To support increasing data-intensive wireless applications, millimeter-wave (mmWave) communication emerges as the most promising wireless technology that offers high data rate connections by exploiting a large swath of spectrum. Beamforming (BF) that focuses the radio frequency power in a narrow direction, is adopted in mmWave communication to overcome the hostile path loss. However, the distinct high directionality feature caused by BF poses new challenges: 1) Beam alignment (BA) latency which is a processing delay that both the transmitter and the receiver align their beams to establish a reliable link. Existing BA methods incur significant BA latency on the order of seconds for a large number of beams; 2) Medium access control (MAC) degradation. To coordinate the BF training for multiple users, 802.11ad standard specifies a new MAC protocol in which all the users contend for BF training resources in a distributed manner. Due to the “deafness” problem caused by directional transmission, i.e., a user may not sense the transmission of other users, severe collisions occur in high user density scenarios, which significantly degrades the MAC performance; and 3) Backhaul congestion. All the base stations (BSs) in mmWave dense networks are connected to backbone network via backhaul links, in order to access remote content servers. Although BF technology can increase the data rate of the fronthaul links between users and the BS, the congested backhaul link becomes a new bottleneck, since deploying unconstrained wired backhaul links in mmWave dense networks is infeasible due to high costs. In this dissertation, we address each challenge respectively by 1) proposing an efficient BA algorithm; 2) evaluating and enhancing the 802.11ad MAC performance; and 3) designing an effective backhaul alleviation scheme. Firstly, we propose an efficient BA algorithm to reduce processing latency. The existing BA methods search the entire beam space to identify the optimal transmit-receive beam pair, which leads to significant latency. Thus, an efficient BA algorithm without search- ing the entire beam space is desired. Accordingly, a learning-based BA algorithm, namely hierarchical BA (HBA) algorithm is proposed which takes advantage of the correlation structure among beams such that the information from nearby beams is extracted to iden- tify the optimal beam, instead of searching the entire beam space. Furthermore, the prior knowledge on the channel fluctuation is incorporated in the proposed algorithm to further accelerate the BA process. Theoretical analysis indicates that the proposed algorithm can effectively identify the optimal beam pair with low latency. Secondly, we analyze and enhance the performance of BF training MAC (BFT-MAC) in 802.11ad. Existing analytical models for traditional omni-directional systems are un- suitable for BFT-MAC due to the distinct directional transmission feature in mmWave networks. Therefore, a thorough theoretical framework on BFT-MAC is necessary and significant. To this end, we develop a simple yet accurate analytical model to evaluate the performance of BFT-MAC. Based on our analytical model, we derive the closed-form expressions of average successful BF training probability, the normalized throughput, and the BF training latency. Asymptotic analysis indicates that the maximum normalized throughput of BFT-MAC is barely 1/e. Then, we propose an enhancement scheme which adaptively adjusts MAC parameters in tune with user density. The proposed scheme can effectively improve MAC performance in high user density scenarios. Thirdly, to alleviate backhaul burden in mmWave dense networks, edge caching that proactively caches popular contents at the edge of mmWave networks, is employed. Since the cache resource of an individual BS can only store limited contents, this significantly throttles the caching performance. We propose a cooperative edge caching policy, namely device-to-device assisted cooperative edge caching (DCEC), to enlarge cached contents by jointly utilizing cache resources of adjacent users and BSs in proximity. In addition, the proposed caching policy brings an extra advantage that the high directional transmission in mmWave communications can naturally tackle the interference issue in the cooperative caching policy. We theoretically analyze the performance of DCEC scheme taking the network density, the practical directional antenna model and the stochastic information of network topology into consideration. Theoretical results demonstrate that the proposed policy can achieve higher performance in offloading the backhaul traffic and reducing the content retrieval delay, compared with the benchmark policy. The research outcomes from the dissertation can provide insightful lights on under- standing the fundamental performance of the mmWave networks from the perspectives of BA, MAC, and backhaul. The schemes developed in the dissertation should offer practical and efficient solutions to build and optimize the mmWave networks