A 3D-collaborative wireless network: towards resilient communication for rescuing flood victims

Every year, floods result in huge damage and devastation both to lives and properties all over the world. Much of this devastation and its prolonged effects result from a lack of collaboration among the rescue agents as a consequence of the lack of reliable and resilient communication platform in the disrupted and damaged environments. In order to counteract this issue, this paper aims to propose a three-dimensional (3D)- collaborative wireless network utilizing air, water and ground based communication infrastructures to support rescue missions in flood-affected areas. Through simulated Search and Rescue(SAR) activities, the effectiveness of the proposed network model is validated and its superiority over the traditional SAR is demonstrated, particularly in the harsh flood environments. The model of the 3D-Collaborative wireless network is expected to significantly assist the rescuing teams in accomplishing their task more effectively in the corresponding disaster areas

Collaborative Uploading in Heterogeneous Networks: Optimal and Adaptive Strategies

Collaborative uploading describes a type of crowdsourcing scenario in networked environments where a device utilizes multiple paths over neighboring devices to upload content to a centralized processing entity such as a cloud service. Intermediate devices may aggregate and preprocess this data stream. Such scenarios arise in the composition and aggregation of information, e.g., from smartphones or sensors. We use a queuing theoretic description of the collaborative uploading scenario, capturing the ability to split data into chunks that are then transmitted over multiple paths, and finally merged at the destination. We analyze replication and allocation strategies that control the mapping of data to paths and provide closed-form expressions that pinpoint the optimal strategy given a description of the paths' service distributions. Finally, we provide an online path-aware adaptation of the allocation strategy that uses statistical inference to sequentially minimize the expected waiting time for the uploaded data. Numerical results show the effectiveness of the adaptive approach compared to the proportional allocation and a variant of the join-the-shortest-queue allocation, especially for bursty path conditions.Comment: 15 pages, 11 figures, extended version of a conference paper accepted for publication in the Proceedings of the IEEE International Conference on Computer Communications (INFOCOM), 201

Community computation

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Materials Science and Engineering, 2009.Cataloged from PDF version of thesis.Includes bibliographical references (p. 171-186).In this thesis we lay the foundations for a distributed, community-based computing environment to tap the resources of a community to better perform some tasks, either computationally hard or economically prohibitive, or physically inconvenient, that one individual is unable to accomplish efficiently. We introduce community coding, where information systems meet social networks, to tackle some of the challenges in this new paradigm of community computation. We design algorithms, protocols and build system prototypes to demonstrate the power of community computation to better deal with reliability, scalability and security issues, which are the main challenges in many emerging community-computing environments, in several application scenarios such as community storage, community sensing and community security. For example, we develop a community storage system that is based upon a distributed P2P (peer-to-peer) storage paradigm, where we take an array of small, periodically accessible, individual computers/peer nodes and create a secure, reliable and large distributed storage system. The goal is for each one of them to act as if they have immediate access to a pool of information that is larger than they could hold themselves, and into which they can contribute new stuff in a both open and secure manner. Such a contributory and self-scaling community storage system is particularly useful where reliable infrastructure is not readily available in that such a system facilitates easy ad-hoc construction and easy portability. In another application scenario, we develop a novel framework of community sensing with a group of image sensors. The goal is to present a set of novel tools in which software, rather than humans, examines the collection of images sensed by a group of image sensors to determine what is happening in the field of view. We also present several design principles in the aspects of community security. In one application example, we present community-based email spain detection approach to deal with email spams more efficiently.by Fulu Li.Ph.D

Design and Analysis of an Efficient Friend-to-Friend Content Dissemination System

International audienceOpportunistic communication, off-loading and decentrlaized distribution have been proposed as a means of cost efficient disseminating content when users are geographically clustered into communities. Despite its promise, none of the proposed systems have not been widely adopted due to unbounded high content delivery latency, security and privacy concerns. This paper, presents a novel hybrid content storage and distribution system addressing the trust and privacy concerns of users, lowering the cost of content distribution and storage, and shows how they can be combined uniquely to develop mobile social networking services. The system exploit the fact that users will trust their friends, and by replicating content on friends’ devices who are likely to consume that content it will be possible to disseminate it to other friends when connected to low cost networks. The paper provides a formal definition of this content replication problem, and show that it is NP hard. Then, it presents a community based greedy heuristic algorithm with novel dynamic centrality metrics that replicates the content on a minimum number of friends’ devices, to maximize availability. Then using both real world and synthetic datasets, the effectiveness of the proposed scheme is demonstrated. The practicality of the proposed system, is demonstrated through an implementation on Android smartphones

Resource Allocation Framework in Fog Computing for the Internet of Things Environments

Fog computing plays a pivotal role in the Internet of Things (IoT) ecosystem because of its ability to support delay-sensitive tasks, bringing resources from cloud servers closer to the “ground” and support IoT devices that are resource-constrained. Although fog computing offers some benefits such as quick response to requests, geo-distributed data processing and data processing in the proximity of the IoT devices, the exponential increase of IoT devices and large volumes of data being generated has led to a new set of challenges. One such problem is the allocation of resources to IoT tasks to match their computational needs and quality of service (QoS) requirements, whilst meeting both task deadlines and user expectations. Most proposed solutions in existing works suggest task offloading mechanisms where IoT devices would offload their tasks randomly to the fog layer or cloud layer. This helps in minimizing the communication delay; however, most tasks would end up missing their deadlines as many delays are experienced during offloading. This study proposes and introduces a Resource Allocation Scheduler (RAS) at the IoT-Fog gateway, whose goal is to decide where and when a task is to be offloaded, either to the fog layer, or the cloud layer based on their priority needs, computational needs and QoS requirements. The aim directly places work within the communication networks domain, in the transport layer of the Open Systems Interconnection (OSI) model. As such, this study follows the four phases of the top-down approach because of its reusability characteristics. To validate and test the efficiency and effectiveness of the RAS, the fog framework was implemented and evaluated in a simulated smart home setup. The essential metrics that were used to check if round-trip time was minimized are the queuing time, offloading time and throughput for QoS. The results showed that the RAS helps to reduce the round-trip time, increases throughput and leads to improved QoS. Furthermore, the approach addressed the starvation problem, a phenomenon that tends to affect low priority tasks. Most importantly, the results provides evidence that if resource allocation and assignment are appropriately done, round-trip time can be reduced and QoS can be improved in fog computing. The significant contribution of this research is the novel framework which minimizes round-trip time, addresses the starvation problem and improves QoS. Moreover, a literature reviewed paper which was regarded by reviewers as the first, as far as QoS in fog computing is concerned was produced

Resource Allocation Framework in Fog Computing for the Internet of Things Environments

Fog computing plays a pivotal role in the Internet of Things (IoT) ecosystem because of its ability to support delay-sensitive tasks, bringing resources from cloud servers closer to the “ground” and support IoT devices that are resource-constrained. Although fog computing offers some benefits such as quick response to requests, geo-distributed data processing and data processing in the proximity of the IoT devices, the exponential increase of IoT devices and large volumes of data being generated has led to a new set of challenges. One such problem is the allocation of resources to IoT tasks to match their computational needs and quality of service (QoS) requirements, whilst meeting both task deadlines and user expectations. Most proposed solutions in existing works suggest task offloading mechanisms where IoT devices would offload their tasks randomly to the fog layer or cloud layer. This helps in minimizing the communication delay; however, most tasks would end up missing their deadlines as many delays are experienced during offloading. This study proposes and introduces a Resource Allocation Scheduler (RAS) at the IoT-Fog gateway, whose goal is to decide where and when a task is to be offloaded, either to the fog layer, or the cloud layer based on their priority needs, computational needs and QoS requirements. The aim directly places work within the communication networks domain, in the transport layer of the Open Systems Interconnection (OSI) model. As such, this study follows the four phases of the top-down approach because of its reusability characteristics. To validate and test the efficiency and effectiveness of the RAS, the fog framework was implemented and evaluated in a simulated smart home setup. The essential metrics that were used to check if round-trip time was minimized are the queuing time, offloading time and throughput for QoS. The results showed that the RAS helps to reduce the round-trip time, increases throughput and leads to improved QoS. Furthermore, the approach addressed the starvation problem, a phenomenon that tends to affect low priority tasks. Most importantly, the results provides evidence that if resource allocation and assignment are appropriately done, round-trip time can be reduced and QoS can be improved in fog computing. The significant contribution of this research is the novel framework which minimizes round-trip time, addresses the starvation problem and improves QoS. Moreover, a literature reviewed paper which was regarded by reviewers as the first, as far as QoS in fog computing is concerned was produced

Federated Learning in Mobile Edge Networks: A Comprehensive Survey

In recent years, mobile devices are equipped with increasingly advanced sensing and computing capabilities. Coupled with advancements in Deep Learning (DL), this opens up countless possibilities for meaningful applications. Traditional cloudbased Machine Learning (ML) approaches require the data to be centralized in a cloud server or data center. However, this results in critical issues related to unacceptable latency and communication inefficiency. To this end, Mobile Edge Computing (MEC) has been proposed to bring intelligence closer to the edge, where data is produced. However, conventional enabling technologies for ML at mobile edge networks still require personal data to be shared with external parties, e.g., edge servers. Recently, in light of increasingly stringent data privacy legislations and growing privacy concerns, the concept of Federated Learning (FL) has been introduced. In FL, end devices use their local data to train an ML model required by the server. The end devices then send the model updates rather than raw data to the server for aggregation. FL can serve as an enabling technology in mobile edge networks since it enables the collaborative training of an ML model and also enables DL for mobile edge network optimization. However, in a large-scale and complex mobile edge network, heterogeneous devices with varying constraints are involved. This raises challenges of communication costs, resource allocation, and privacy and security in the implementation of FL at scale. In this survey, we begin with an introduction to the background and fundamentals of FL. Then, we highlight the aforementioned challenges of FL implementation and review existing solutions. Furthermore, we present the applications of FL for mobile edge network optimization. Finally, we discuss the important challenges and future research directions in F

Instantly Decodable Network Coding: From Centralized to Device-to-Device Communications

From its introduction to its quindecennial, network coding has built a strong reputation for enhancing packet recovery and achieving maximum information flow in both wired and wireless networks. Traditional studies focused on optimizing the throughput of the system by proposing elaborate schemes able to reach the network capacity. With the shift toward distributed computing on mobile devices, performance and complexity become both critical factors that affect the efficiency of a coding strategy. Instantly decodable network coding presents itself as a new paradigm in network coding that trades off these two aspects. This paper review instantly decodable network coding schemes by identifying, categorizing, and evaluating various algorithms proposed in the literature. The first part of the manuscript investigates the conventional centralized systems, in which all decisions are carried out by a central unit, e.g., a base-station. In particular, two successful approaches known as the strict and generalized instantly decodable network are compared in terms of reliability, performance, complexity, and packet selection methodology. The second part considers the use of instantly decodable codes in a device-to-device communication network, in which devices speed up the recovery of the missing packets by exchanging network coded packets. Although the performance improvements are directly proportional to the computational complexity increases, numerous successful schemes from both the performance and complexity viewpoints are identified