Collaborative Downloading Research Articles

Exploring the role of edge computing on the legal effect of secure collaborative download protocol

To make people's car driving experience safer and more comfortable, the architecture of intrusion recognition model based on the K-nearest neighbour classification Deep Neural Networks (K-DNN) is proposed to classify and identify various network intrusion factors, thereby strengthening the security level of Internet of Vehicles (IoV) and alleviating the hardware resource scarcity for IoV. Then, a secure collaborative download system based on edge computing is proposed, which can accurately and timely collect the information of roads, vehicles and nearby infrastructure in the driving process and facilitate people to safely and quickly download the required content. In the proposed system, the vehicle encrypts the content download request and sends it to the Roadside Unit (RSU) and the content server, respectively. The content server sends the content corresponding to the download request to the vehicle through the RSU.

Exploring the role of edge computing on the legal effect of secure collaborative download protocol

Exploring the role of edge computing on the legal effect of secure collaborative download protocol

Energy-efficiently collaborative data downloading in optical satellite networks

Optical satellite networks (OSNs) play an important role in space applications since it has some merits such as high data rate and low power. Increasing space applications generate a large amount of data on satellite so that satellites have difficulty in downloading these data to ground station timely under the limited contact window. Thus, we investigate the problem of collaborative data downloading in this paper. The problem lies on how to energy-efficiently offload data by optical inter-satellite links (ISLs) to balance data load and downlink capacity of each satellite so as to improve the performance of data downloading in OSN. Firstly, we develop the Cost Time-evolving Graph (CTEG) to describe optical transmission cost and the time-varying topology of OSN. Secondly, the problem of collaborative data downloading is formulated as Multi-objective Mixed-integer Linear Programming (MOMILP) which is proven to be NP-hard. For reducing computational complexity, we divide the scheduling time into multiple stages and propose the Multi-stage Collaborative Scheduling Algorithm (MCSA) which operates on a slot-by-slot basis. Simulations are conducted in the joint platform and results demonstrate that, compared with CoDld and No-ISLs, the MCSA provides a relatively high data throughput meanwhile drastically reducing energy consumption generated by data offloading.

UStash: A Novel Mobile Content Delivery System for Improving User QoE in Public Transport

Mobile data traffic is growing exponentially and it is even more challenging to distribute content efficiently while users are “on the move” such as in public transport. The use of mobile devices for accessing content (e.g., videos) while commuting are both expensive and unreliable, although it is becoming common practice worldwide. Leveraging on the spatial and temporal correlation of content popularity and users’ diverse network connectivity, we propose a novel content distribution system, uStash, which guarantees better QoE with regards to access delays and cost of usage. The proposed collaborative download and content stashing schemes provide the uStash provider the flexibility to control the cost of content access via cellular networks. We model the uStash system in a probabilistic framework and thereby analytically derive the optimal portions for collaborative downloading. Then, we validate the proposed models using real-life trace driven simulations. In particular, we use dataset from 22 inter-city buses running on six different routes and from a mobile VoD service provider to show that uStash reduces the cost of monthly cellular data by approximately 50 percent and the expected delay for content access by 60 percent compared to content downloaded via users’ cellular network connections.

CTRUST: A Dynamic Trust Model for Collaborative Applications in the Internet of Things

Security through trust presents a viable solution for threat management in the Internet of Things (IoT). Currently, a well-defined trust management framework for collaborative applications on the IoT platform does not exist. In order to estimate reliably the trust values of nodes within a system, the trust should be measured by suitable parameters that are based on the nodes’ functional properties in the application context. Existing models do not clearly outline the parametrization of trust. Also, trust decay is inadequately modeled in most current models. In addition, trust recommendations are usually inaccurately weighted with respect to previous trust, thereby increasing the effect of bad recommendations. A new model, CTRUST, is proposed to resolve these shortcomings. In CTRUST, trust is accurately parametrized while recommendations are evaluated through belief functions. The effects of trust decay and maturity on the trust evaluation process were studied. Each trust component is neatly modeled by appropriate mathematical functions. CTRUST was implemented in a collaborative download application and its performance was evaluated based on the utility derived and its trust accuracy, convergence, and resiliency. The results indicate that IoT collaborative applications based on CTRUST gain a significant improvement in performance, in terms of efficiency and security.

Collaborative Content Downloading in VANETs with Fuzzy Comprehensive Evaluation

Vehicle collaborative content downloading has become a hotspot in current vehicular ad-hoc network (VANET) research. However, in reality, the highly dynamic nature of VANET makes users lose resources easily, and the transmission of invalid segment data also wastes valuable bandwidth and storage of the users’ vehicles. In addition, the individual need of each customer vehicle should also be taken into consideration when selecting an agent vehicle for downloading. In this paper, a novel scheme is proposed for vehicle selection in the download of cooperative content from the Internet, by considering the basic evaluation information of the vehicle. To maximize the overall throughput of the system, a collaborative content downloading algorithm is proposed, which is based on fuzzy evaluation and a customer’s own expectations, in order to solve the problems of agent vehicle selection. With the premise of ensuring successful downloading and the selection preferences of customer vehicles, linear programming is used to optimize the distribution of agent vehicles and maximize customer’s satisfaction. Simulation results show that the proposed scheme works well in terms of average quality of service, average bandwidth efficiency, failure frequency, and average consumption.

A Proxy-Based Collaboration System to Minimize Content Download Time and Energy Consumption

Mobile collaborative community (MCC) is an emerging technology that allows multiple mobile nodes (MNs) to perform a resource intensive task, such as large content download, in a cooperative manner. In this paper, we introduce a proxy-based collaboration system for the MCC where a content proxy (CProxy) determines the amount of chunks and the sharing order scheduled to each MN, and the received chunks are shared among MNs via Wi-Fi Direct. We formulate a multi-objective optimization problem to minimize both the collaborative content download time and the energy consumption in an MCC, and propose a heuristic algorithm for solving the optimization problem. Extensive simulations are carried out to evaluate the effects of the number of MNs, the wireless bandwidth, the content size, and dynamic channel conditions on the content download time and the energy consumption. Our results demonstrate that the proposed algorithm can achieve near-optimal performance and significantly reduce the content download time and has an energy consumption comparable to that of other algorithms.

Efficient content delivery through fountain coding in opportunistic information-centric networks

Opportunistic networks can increase network capacity, support collaborative downloading of content and offload traffic from a cellular to a cellular-assisted, device-to-device network. They can also support communication and content exchange when the cellular infrastructure is under severe stress and when the network is down or inaccessible. Fountain coding has been considered as especially suitable for lossy networks, providing reliable multicast transport without requiring feedback from receivers. It is also ideal for multi-path and multi-source communication that fits exceptionally well with opportunistic networks. In this paper, we propose a content-centric approach for disseminating content in opportunistic networks efficiently and reliably. Our approach is based on Information-Centric Networking (ICN) and employs fountain coding. When tied together, ICN and fountain coding provide a comprehensive solution that overcomes significant limitations of existing approaches. Extensive network simulations indicate that our approach is viable. Cache hit ratio can be increased by up to five times, while the overall network traffic load is reduced by up to four times compared to content dissemination on top of the standard Named Data Networking architecture.

ECDS: Efficient collaborative downloading scheme for popular content distribution in urban vehicular networks

The recent development of the Vehicular Ad-hoc Networks (VANETs) has motivated an increasing interest in in-vehicle consumption, and hence, the Popular Content Distribution (PCD) has become a heated issue. Compared with PCD solutions based on the widely-used cellular networks and Dedicated Short Range Communications (DSRC), solutions based on Collaborative Downloading (CD) are more economical and efficient. Due to the limited bandwidth, the On-Board Units (OBUs) passing through a Road Side Unit (RSU) can only download a portion of the popular content. To get over that drawback and to effect a collaborative downloading, a P2P network should be constructed among the OBUs which fall out of the RSUs coverage. In this paper, we address the efficient collaborative downloading scheme (ECDS) for PCD in urban traffic scenarios. To adapt to the rapid-changing characteristics of the VANET topology, a new cell-based clustering scheme is proposed, which greatly simplifies the modeling. Besides a strategy of inter-cluster Relay Selection is proposed to construct a pear-to-pear (P2P) network of scale-free property, which will help enhancing the information spread. Furthermore, another inter-cluster strategy of generation selection is to be collaborated to accelerate the dissemination process in the P2P network. The comparison experiments to two up-to-date collaborative PCD protocols demonstrate the high performance of the proposed scheme, i.e. ECDS.

Swarm-oriented mobile services: Step towards green communication

Green communication is a new focus within the telecommunications industry, leading to various innovative ideas on how to optimize communication in order to achieve energy savings. While the majority of those ideas target the transport layer of communication systems, this paper takes a different approach and proposes an innovation for the application layer by introducing a novel concept in the mobile telecom service provisioning process: the swarm-oriented services. The presented proof-of-concept swarm-oriented mobile telecom service is called the Collaborative Downloading and its primary goal is to lower the mobile users’ overall energy consumption while they are downloading data. In order to enable this service, we designed the self-organizing market-based algorithm (SOMA), which combines swarm intelligence (i.e. self-organization) and market-based (i.e. auctions) mechanisms, while the proof-of-concept implementation is based on multi-agent technology. Analytical results show that the proposed Collaborative Downloading service saved up to 76% of energy in mobile device batteries when compared with the current mobile data download practice (in a scenario with 6 mobile users whose mobile devices support GPRS and Bluetooth networking technologies).

COLLABORATIVE URBAN COMPUTING: SERENDIPITOUS COOPERATION BETWEEN USERS IN AN URBAN ENVIRONMENT

Recent technical advancements in computing devices and communication networks, combined with changes in the way people use information and communication technology (ICT) systems, have transformed the ICT industry. In this article, we present the collaborative urban computing (CUC) paradigm, which supports serendipitous cooperation between a set of users physically located in an urban environment and all sharing a common goal. Advantages and disadvantages of the CUC paradigm are elaborated on a proof-of-concept CUC service, which we call collaborative downloading.

Slicing Distributed Systems

Peer-to-peer (P2P) architectures are popular for tasks such as collaborative download, VoIP telephony, and backup. To maximize performance in the face of widely variable storage capacities and bandwidths, such systems typically need to shift work from poor nodes to richer ones. Similar requirements are seen in today's large data centers, where machines may have widely variable configurations, loads, and performance. In this paper, we consider the slicing problem, which involves partitioning the participating nodes into k subsets using a one-dimensional attribute, and updating the partition as the set of nodes and their associated attributes change. The mechanism thus facilitates the development of adaptive systems. We begin by motivating this problem statement and reviewing prior work. Existing algorithms are shown to have problems with convergence, manifesting as inaccurate slice assignments, and to adapt slowly as conditions change. Our protocol, Sliver, has provably rapid convergence, is robust under stress and is simple to implement. We present both theoretical and experimental evaluations of the protocol.

Joint uplink/downlink opportunistic scheduling for Wi-Fi WLANs

Recent advances in the speed of multi-rate wireless local area networks (WLANs) and the proliferation of WLAN devices have made rate adaptive, opportunistic scheduling critical for throughput optimization. As WLAN traffic evolves to be more symmetric due to the emerging new applications such as VoWLAN, collaborative download, and peer-to-peer file sharing, opportunistic scheduling at the downlink becomes insufficient for optimized utilization of the single shared wireless channel. Furthermore, without proper scheduling on the uplink, the downlink throughput gain diminishes proportionally to the increasing number of clients transmitting on the uplink. However, opportunistic scheduling on the uplink of a WLAN is challenging because wireless channel condition is dynamic and asymmetric. Each transmitting client has to probe the access point to maintain the updated channel conditions at the access point. Moreover, the scheduling decisions must be coordinated at all clients for consistency. This paper presents JUDS, a joint uplink/downlink opportunistic scheduling for WLANs. Through synergistic integration of both the uplink and the downlink scheduling, JUDS maximizes channel diversity at significantly reduced scheduling overhead. It also enforces fair channel sharing between the downlink and uplink traffic. Through analysis and extensive QualNet simulations, we show that JUDS improves the overall throughput by up to 127% and achieves both fairness between uplink and downlink traffic.