Temporal random walk as a lightweight communication infrastructure for opportunistic networks

This paper explores the idea of sharing a common storage unit (token) as a lightweight communication infrastructure for opportunistic networks. Instead of using contacts as opportunities to transfer messages, we use them to pass the token over time. We implement a Temporal Random Walk (TRW) process to support such evolution. Sending a message is equivalent to copying it in the token and passing the token to a connected node. Eventually the recipient node will get the token and all its addressed messages. We study our approach using both synthetic and real traces. We show that it can be equivalent to common routing strategies in terms of delivery ratio and delay

Characterization and Applications of Temporal Random Walks on Opportunistic Networks

Opportunistic networks are a special case of DTN that exploit systematically the mobility of nodes. When nodes contacts occur, routing protocols can exploit them to forward messages. In the absence of stable end-to-end paths, spatio-temporal paths are created spontaneously. Opportunistic networks are suitable for communications in pervasive environments that are saturated by other devices. The ability to self-organize using the local interactions among nodes, added to mobility, leads to a shift from legacy packet-based communications towards a message-based communication paradigm. Usually, routing is done by means of message replication in order to increase the probability of message delivery. Instead, we study the useof Temporal Random Walks (TRW) on opportunistic networks as a simple method to deliver messages. TRW can adapt itself to the self-organizing evolution of opportunistic networks. A TRW can be seen as the passing of a token among nodes on the spatio-temporal paths. Since the token passing is an atomic operation, we can see it as forwarding one simple message among nodes. We study the drop ratio for message forwarding considering finite buffers. We then explore the idea of token-sharing as a routing mechanism. Instead of using contacts as mere opportunities to transfer messages, we use them to forward the token over time. The evolution of the token is ruled by the TRW process. Finally, we use the TRW to monitor opportunistic networks. We present the limits and convergence of monitoring the interact time between participating nodes

Applications of Temporal Random Walks over Opportunistic Networks

Delay tolerant networks (DTNs) were introduced to deal with environments where interruptions or disruptions of service were expected. Such networks usually lack of end-to-end paths or any infrastructure to help communications. Usually, there is neither guarantee about the availability of the connections nor the topology of the network. Opportunistic networks are a special case of DTN that exploit systematically the mobility of nodes. When nodes contacts occur, routing protocols can exploit them to forward messages. In the absence of stable end-to-end paths, spatio-temporal paths are created opportunistically. Opportunistic networks are suitable for communications in pervasive environments that are saturated by other devices. The ability to self-organize using the local interactions among nodes, added to mobility, leads to a shift from legacy packet-based communications towards a message-based communication paradigm. Opportunistic networks can be associated with human networks where people are moving around. In this work, we study the use of Temporal Random Walks (TRW) as a simple method that can adapt itself to the self-organizing evolution of opportunistic networks

Tag-assisted social-aware opportunistic device-to-device sharing for traffic offloading in mobile social networks

Within recent years, the service demand for rich multimedia over mobile networks has kept being soaring at a tremendous pace. To solve the critical problem of mobile traffic explosion, substantial efforts have been made from researchers to try to offload the mobile traffic from infrastructured cellular links to direct short-range communications locally among nearby users. In this article, we discuss the potential of combining users’ online and offline social impacts to exploit the device-to-device (D2D) opportunistic sharing for offloading the mobile traffic. We propose Tag-Assisted Social-Aware D2D sharing framework, TASA, with corresponding optimization models, architecture design, and communication protocols. Through extensive simulations based on real data traces, we demonstrate that TASA can offload up to 78.9% of the mobile traffic effectively

Impacts of Mobility Models on RPL-Based Mobile IoT Infrastructures: An Evaluative Comparison and Survey

With the widespread use of IoT applications and the increasing trend in the number of connected smart devices, the concept of routing has become very challenging. In this regard, the IPv6 Routing Protocol for Low-power and Lossy Networks (PRL) was standardized to be adopted in IoT networks. Nevertheless, while mobile IoT domains have gained significant popularity in recent years, since RPL was fundamentally designed for stationary IoT applications, it could not well adjust with the dynamic fluctuations in mobile applications. While there have been a number of studies on tuning RPL for mobile IoT applications, but still there is a high demand for more efforts to reach a standard version of this protocol for such applications. Accordingly, in this survey, we try to conduct a precise and comprehensive experimental study on the impact of various mobility models on the performance of a mobility-aware RPL to help this process. In this regard, a complete and scrutinized survey of the mobility models has been presented to be able to fairly justify and compare the outcome results. A significant set of evaluations has been conducted via precise IoT simulation tools to monitor and compare the performance of the network and its IoT devices in mobile RPL-based IoT applications under the presence of different mobility models from different perspectives including power consumption, reliability, latency, and control packet overhead. This will pave the way for researchers in both academia and industry to be able to compare the impact of various mobility models on the functionality of RPL, and consequently to design and implement application-specific and even a standard version of this protocol, which is capable of being employed in mobile IoT applications

Effective and Efficient Communication and Collaboration in Participatory Environments

Participatory environments pose significant challenges to deploying real applications. This dissertation investigates exploitation of opportunistic contacts to enable effective and efficient data transfers in challenged participatory environments. There are three main contributions in this dissertation: 1. A novel scheme for predicting contact volume during an opportunistic contact (PCV); 2. A method for computing paths with combined optimal stability and capacity (COSC) in opportunistic networks; and 3. An algorithm for mobility and orientation estimation in mobile environments (MOEME). The proposed novel scheme called PCV predicts contact volume in soft real-time. The scheme employs initial position and velocity vectors of nodes along with the data rate profile of the environment. PCV enables efficient and reliable data transfers between opportunistically meeting nodes. The scheme that exploits capacity and path stability of opportunistic networks is based on PCV for estimating individual link costs on a path. The total path cost is merged with a stability cost to strike a tradeoff for maximizing data transfers in the entire participatory environment. A polynomial time dynamic programming algorithm is proposed to compute paths of optimum cost. We propose another novel scheme for Real-time Mobility and Orientation Estimation for Mobile Environments (MOEME), as prediction of user movement paves way for efficient data transfers, resource allocation and event scheduling in participatory environments. MOEME employs the concept of temporal distances and uses logistic regression to make real time estimations about user movement. MOEME relies only on opportunistic message exchange and is fully distributed, scalable, and requires neither a central infrastructure nor Global Positioning System. Indeed, accurate prediction of contact volume, path capacity and stability and user movement can improve performance of deployments. However, existing schemes for such estimations make use of preconceived patterns or contact time distributions that may not be applicable in uncertain environments. Such patterns may not exist, or are difficult to recognize in soft-real time, in open environments such as parks, malls, or streets

Defending against Sybil Devices in Crowdsourced Mapping Services

Real-time crowdsourced maps such as Waze provide timely updates on traffic, congestion, accidents and points of interest. In this paper, we demonstrate how lack of strong location authentication allows creation of software-based {\em Sybil devices} that expose crowdsourced map systems to a variety of security and privacy attacks. Our experiments show that a single Sybil device with limited resources can cause havoc on Waze, reporting false congestion and accidents and automatically rerouting user traffic. More importantly, we describe techniques to generate Sybil devices at scale, creating armies of virtual vehicles capable of remotely tracking precise movements for large user populations while avoiding detection. We propose a new approach to defend against Sybil devices based on {\em co-location edges}, authenticated records that attest to the one-time physical co-location of a pair of devices. Over time, co-location edges combine to form large {\em proximity graphs} that attest to physical interactions between devices, allowing scalable detection of virtual vehicles. We demonstrate the efficacy of this approach using large-scale simulations, and discuss how they can be used to dramatically reduce the impact of attacks against crowdsourced mapping services.Comment: Measure and integratio

Total order in opportunistic networks

Opportunistic network applications are usually assumed to work only with unordered immutable messages, like photos, videos, or music files, while applications that depend on ordered or mutable messages, like chat or shared contents editing applications, are ignored. In this paper, we examine how total ordering can be achieved in an opportunistic network. By leveraging on existing dissemination and causal order algorithms, we propose a commutative replicated data type algorithm on the basis of Logoot for achieving total order without using tombstones in opportunistic networks where message delivery is not guaranteed by the routing layer. Our algorithm is designed to use the nature of the opportunistic network to reduce the metadata size compared to the original Logoot, and even to achieve in some cases higher hit rates compared to the dissemination algorithms when no order is enforced. Finally, we present the results of the experiments for the new algorithm by using an opportunistic network emulator, mobility traces, and Wikipedia pages.Peer ReviewedPostprint (author's final draft

Online Multi-Agent Decentralized Byzantine-robust Gradient Estimation

In this paper, we propose an iterative scheme for distributed Byzantineresilient estimation of a gradient associated with a black-box model. Our algorithm is based on simultaneous perturbation, secure state estimation and two-timescale stochastic approximations. We also show the performance of our algorithm through numerical experiments