Socially Trusted Collaborative Edge Computing in Ultra Dense Networks

Small cell base stations (SBSs) endowed with cloud-like computing capabilities are considered as a key enabler of edge computing (EC), which provides ultra-low latency and location-awareness for a variety of emerging mobile applications and the Internet of Things. However, due to the limited computation resources of an individual SBS, providing computation services of high quality to its users faces significant challenges when it is overloaded with an excessive amount of computation workload. In this paper, we propose collaborative edge computing among SBSs by forming SBS coalitions to share computation resources with each other, thereby accommodating more computation workload in the edge system and reducing reliance on the remote cloud. A novel SBS coalition formation algorithm is developed based on the coalitional game theory to cope with various new challenges in small-cell-based edge systems, including the co-provisioning of radio access and computing services, cooperation incentives, and potential security risks. To address these challenges, the proposed method (1) allows collaboration at both the user-SBS association stage and the SBS peer offloading stage by exploiting the ultra dense deployment of SBSs, (2) develops a payment-based incentive mechanism that implements proportionally fair utility division to form stable SBS coalitions, and (3) builds a social trust network for managing security risks among SBSs due to collaboration. Systematic simulations in practical scenarios are carried out to evaluate the efficacy and performance of the proposed method, which shows that tremendous edge computing performance improvement can be achieved.Comment: arXiv admin note: text overlap with arXiv:1010.4501 by other author

Quality of Service improvements for real time multimedia applications using next generation network architectures and blockchain in Internet Service Provider cooperative scenario

Real time communications are becoming part of our daily life, requiring constrained requisites with the purpose of being enjoyed in harmony by end users. The factors ruling these requisites are Quality of Service parameters of the users' Internet connections. Achieving a satisfactory QoS level for real time communications depends on parameters that are strongly influenced by the quality of the network connections among the Internet Service Providers, which are located in the path between final users and Over The Top service providers that are supplying them with real time services. Final users can be: business people having real time videoconferences, or adopting crytpocurrencies in their exchanges, videogamers playing online games together with others residing in other countries, migrants talking with their relatives or watching their children growing up in their home countries, people with disabilities adopting tecnologies to help them, doctors performing remote surgeries, manufacturers adopting augmented reality devices to perform dangerous tasks. Each of them performing their daily activities are requiring specific QoS parameters to their ISPs, that nowadays seem to be unable to provide them with a satisfactory QoS level for these kinds of real time services. Through the adoption of next generation networks, such as the Information Centric Networking, it would be possible to overcome the QoS problems that nowadays are experienced. By adopting Blockchain technologies, in several use cases, it would be possible to improve those security aspects related to the non-temperability of information and privacy. I started this thesis analyzing next generation architectures enabling real time multimedia communications. In Software Defined Networking, Named Data Networking and Community Information Centric Networking, I highlighted potential approaches to solve QoS problems that are affecting real time multimedia applications. During my experiments I found that applications able to transmit high quality videos, such as 4k or 8k videos, or to directly interact with devices AR/VR enabled are missing for both ICN approaches. Then I proposed a REST interface for the enforcing of a specific QoS parameter, the round trip time (RTT) taking into consideration the specific use case of a game company that connects with the same telecommunication company of the final user. Supposing that the proposed REST APIs have been deployed in the game company and in the ISP, when one or more users are experiencing lag, the game company will try to ask the ISP to reduce the RTT for that specific user or that group of users. This request can be done by performing a call to a method where IP address(es) and the maximum RTT desired are passed. I also proposed other methods, through which it would be possible to retrieve information about the QoS parameters, and exchange, if necessary, an exceeding parameter in change of another one. The proposed REST APIs can also be used in more complex scenarios, where ISPs along the path are chained together, in order to improve the end to end QoS among Over The Top service provider and final users. To store the information exchanged by using the proposed REST APIs, I proposed to adopt a permissioned blockchain, analizying the ISPs cooperative use case with Hyperledger Fabric, where I proposed the adoption of the Proof of Authority consensus algorithm, to increase the throughput in terms of transactions per second. In a specific case that I examined, I am proposing a combination of Information Centric Networking and Blockchain, in an architecture where ISPs are exchanging valuable information regarding final Users, to improve their QoS parameters. I also proposed my smart contract for the gaming delay use case, that can be used to rule the communication among those ISPs that are along the path among OTT and final users. An extension of this work can be done, by defining billing costs for the QoS improvements

Named data networking for efficient IoT-based disaster management in a smart campus

Disasters are uncertain occasions that can impose a drastic impact on human life and building infrastructures. Information and Communication Technology (ICT) plays a vital role in coping with such situations by enabling and integrating multiple technological resources to develop Disaster Management Systems (DMSs). In this context, a majority of the existing DMSs use networking architectures based upon the Internet Protocol (IP) focusing on location-dependent communications. However, IP-based communications face the limitations of inefficient bandwidth utilization, high processing, data security, and excessive memory intake. To address these issues, Named Data Networking (NDN) has emerged as a promising communication paradigm, which is based on the Information-Centric Networking (ICN) architecture. An NDN is among the self-organizing communication networks that reduces the complexity of networking systems in addition to provide content security. Given this, many NDN-based DMSs have been proposed. The problem with the existing NDN-based DMS is that they use a PULL-based mechanism that ultimately results in higher delay and more energy consumption. In order to cater for time-critical scenarios, emergence-driven network engineering communication and computation models are required. In this paper, a novel DMS is proposed, i.e., Named Data Networking Disaster Management (NDN-DM), where a producer forwards a fire alert message to neighbouring consumers. This makes the nodes converge according to the disaster situation in a more efficient and secure way. Furthermore, we consider a fire scenario in a university campus and mobile nodes in the campus collaborate with each other to manage the fire situation. The proposed framework has been mathematically modeled and formally proved using timed automata-based transition systems and a real-time model checker, respectively. Additionally, the evaluation of the proposed NDM-DM has been performed using NS2. The results prove that the proposed scheme has reduced the end-to-end delay up from 2% to 10% and minimized up to 20% energy consumption, as energy improved from 3% to 20% compared with a state-of-the-art NDN-based DMS

Crossing Roads of Federated Learning and Smart Grids: Overview, Challenges, and Perspectives

Consumer's privacy is a main concern in Smart Grids (SGs) due to the sensitivity of energy data, particularly when used to train machine learning models for different services. These data-driven models often require huge amounts of data to achieve acceptable performance leading in most cases to risks of privacy leakage. By pushing the training to the edge, Federated Learning (FL) offers a good compromise between privacy preservation and the predictive performance of these models. The current paper presents an overview of FL applications in SGs while discussing their advantages and drawbacks, mainly in load forecasting, electric vehicles, fault diagnoses, load disaggregation and renewable energies. In addition, an analysis of main design trends and possible taxonomies is provided considering data partitioning, the communication topology, and security mechanisms. Towards the end, an overview of main challenges facing this technology and potential future directions is presented