Named-Data Networking (NDN), a realization of the Information-Centric Networking (ICN) vision, offers a request-response communication model where data is identified based on application-defined names at the network layer. This amplifies the ability of censoring authorities to restrict access to certain data/websites/applications and monitor user requests. The majority of existing NDN-based frameworks have focused on enabling users in a censoring network to access data available outside of this network, without considering how data producers in a censoring network can make their data available to users outside of this network. This problem becomes especially challenging, since the NDN communication paths are symmetric, while producers are mandated to sign the data they generate and identify their certificates. In this paper, we propose Harpocrates, an NDN-based framework for anonymous data publication under censorship conditions. Harpocrates enables producers in censoring networks to produce and make their data available to users outside of these networks while remaining anonymous to censoring authorities. Our evaluation demonstrates that Harpocrates achieves anonymous data publication under different settings, being able to identify and adapt to censoring actions.

Due to the proliferation of Internet of Things (IoT) and application/user demands that challenge communication and computation, edge computing has emerged as the paradigm to bring computing resources closer to users. In this paper, we present Whispering, an analytical model for the migration of services (service offloading) from the cloud to the edge, in order to minimize the completion time of computational tasks offloaded by user devices and improve the utilization of resources. We also empirically investigate the impact of reusing the results of previously executed tasks for the execution of newly received tasks (computation reuse) and propose an adaptive task offloading scheme between edge and cloud. Our evaluation results show that Whispering achieves up to 35% and 97% (when coupled with computation reuse) lower task completion times than cases where tasks are executed exclusively at the edge or the cloud.

In today's era of explosion of Internet of Things (IoT) and end-user devices and their data volume emanating at the network's edge, the network should be more in-tune with meeting the needs of these demanding edge computing applications. To this end, we design and prototype Information-Centric edge (ICedge), a general-purpose networking framework that streamlines service invocation and improves the reuse of redundant computation at the edge. ICedge runs on top of named-data networking, a realization of the information-centric networking vision, and handles the “low-level” network communication on behalf of applications. ICedge features a fully distributed design that: 1) enables users to get seamlessly on-boarded onto an edge network; 2) delivers application invoked tasks to edge nodes for execution in a timely manner; and 3) offers naming abstractions and network-based mechanisms to enable (partial or full) reuse of the results of already executed tasks among users, which we call “compute reuse,” resulting in lower task completion times and efficient use of edge computing resources. Our simulation and testbed deployment results demonstrate that ICedge can achieve up to $50\times $ lower task completion times leveraging its network-based compute reuse mechanism compared to cases, where reuse is not available.

The fog computing paradigm brings together storage, communication, and computation resources closer to users' end-devices. Therefore, fog servers are deployed at the edge of the network, offering low latency access to users. With the expansion of such fog computing services, different providers will be able to deploy multiple resources within a restricted geographical proximity. In this paper, we investigate an incentive-based cooperation scheme across fog providers. We propose a distributed cooperative algorithm amongst fog computing providers where fully collaborative fog nodes are subject to different loads. The proposed algorithm leverages the power-of-two result and exploits a cooperation probability, namely the probability that a given provider collaborates by accepting a computation request from another provider, as a mean to achieve a fair cooperation. We adopt an analytical approach based on exploiting a simplified performance model to demonstrate numerically that a set of optimal accepting probabilities exits when the number of server nodes goes to infinity. This result then drives the design of our distributed algorithm. Second, in our experimental approach, we perform a set of simulation analysis to verify the validity of the proposed solution when the number of servers is limited.

Today, with the emergence of various business review sites such as Yelp, Trip Advisor, and Zomato, people can write reviews and provide an assessment (often as 1-5 score rating). The success of a business on the crowd-sourced review platform has taken the form of positive reviews and high star ratings (failure are associated with negative reviews and low star ratings). We often claim that location plays a major role in determining the success or the failure of a given business. This paper attempts to verify this claim and quantifies the impact of location, solely, on business success, using two data sets; a Yelp dataset for business information and reviews, and another Location dataset that gathers location-based information in a city or an area. We perform an empirical study to quantify the impact of (i) relative location to well known landmarks and (ii) parameterized location (such as cost of living in a given zip code), on the success of restaurants. In our study, we found that parameterized location using location characteristic parameters such as housing affordability correlate highly with restaurant success with more than 0.81 correlation ratio. We also observe that the closer the restaurant to a landmark (relative location) the more likelihood it succeeds.

The efforts exploring Named Data Networking (NDN) have mainly focused on addressing the lack of scalable data distribution by today's Internet. In this paper, we argue that NDN offers a richer environment for edge computing applications. We consider a scenario, where applications need to discover the services running in the edge network. We demonstrate the design and implementation of a distributed service discovery mechanism over NDN through an example use-case of a mobile application for vision impairment patient. The paper discusses three main edge computing challenges, namely service discovery, service invocation, and user mobility management, to highlight NDN's architectural advantages for edge computing systems. Experimental results show that our framework design can effectively utilize the available resources at the network edge, being able to satisfy 95-98% of mobile users' service requests.

Fog computing is a key paradigm that brings together shared storage, low latency communication, and computation resources closer to users' end-devices. While most IoT services adopt a three-tier computing architecture, where fog nodes are always probed first before reaching a distant Cloud, collaboration across multi-stakeholder, multi-tenant fog providers remains unexplored. In this paper, we quantitatively highlight the gain which may arise if a collaborative fog computing paradigm is deployed. Next, we propose CICO, an incentive based collaborative mechanism for fog computing networks. CICO regulates a multi-stakeholder, multi-tenant cooperation among fog providers. We present a mathematical model and an experimental approach to make the case for such cooperation paradigm.

Data-driven and collaborative research has become the trend for today's scientific communities, resulting in large- scale datasets being shared and transported through networks every day. Most of these large data transfers use TCP sockets which are known to be limited in long-distance and high-bandwidth scenarios. UDP, on the other hand, while fast and efficient does not implement any reliability mechanisms. In this paper, we investigate the use of erasure coding techniques, namely fountain codes, on top of UDP to help high speed and reliable data transfer applications to attain high bandwidth in the face of packet losses. We propose RC-UDP, a Raptor Code over UDP framework that enables reliable data transfers for high bandwidth networks. We implement RC-UDP and evaluate its performance using computer simulation (ns-3) and real world testbed experimentations. We compare RC-UDP to HighSpeed and CUBIC TCP. Our results show that RC-UDP, which achieves up to 75X time reduction while incurring minimum overhead, is beneficial when the network is subject to high congestion or packet drop rates.

With the advancements in mobile sensors and health-care technologies, mobile health (mHealth) services are growing in demand. However, the deployment of mHealth's applications in rural and underdeveloped areas remains a major challenge, despite the investments made, largely due to unreliable communication infrastructures. In this paper, we propose delivering mHealth data in a highly disruptive wireless network using resource-limited mobile devices. We build on state-of-the-art opportunistic/DTN solutions, and propose two dynamic schemes that adapt to the level of congestion in the network. Our adaptive forwarding schemes dynamically tune data replication at forwarder nodes by engaging the most appropriate forwarding strategy at any given state, while incurring minimal overhead. In order to achieve such a goal, we propose a reactive and proactive approach to detecting or predicting congestion in the network respectively. We perform a set of data-driven simulations to compare the performance of our proposed schemes with state-of-the-art DTN forwarding algorithms. Our results show that our schemes achieve better delivery ratio for realistic mHealth applications in challenged networking environments.

The proliferation of mobile health (mHealth) technologies in rural and disconnected areas has enabled novel communication challenges. These challenges exist due to the underdeveloped Internet infrastructure, wireless intermittent connectivity, and the large data generated by such applications that often overwhelms the weak infrastructure. In this paper, we investigate data delivery in such overloaded and sparse rural area scenarios, where patients deploy an mHealth service over a disruption-tolerant network (DTN). To quantify the feasibility and the challenges of such communication scenario, we investigate the performance of multiple opportunistic forwarding algorithms under different environments and loads. We highlight the shortcoming of most of these algorithms with regards to the lack of efficient resource management algorithms and the large overhead introduced by such state-of-the-art algorithms. Our data driven experiments highlight such limitations while comparing the performance of three classes of algorithms; flooding based, resource-aware, and controlled flooding algorithms.

We present an empirical study for device-to-device single hop wireless communication. We perform realistic indoor and outdoor measurements of WiFi and Bluetooth communication between representative mobile and IoT devices. This measurement paper aims at answering multiple questions: What are the methods and the limitations in D2D wireless communications for multiple IoT devices? What is the impact of distance/obstacles, environment, and device types on TCP and UDP data transfers? We address these questions, and more, by conducting experimental measurements to cover varying performance aspects such as effective throughput, packet loss ratio, received signal strength (RSSI), and end-to-end delay. Our study presents insights and awareness of the true communication performance of different IoT devices and wireless communication technologies.

Due to the exponential advancements in mobile and IoT device capabilities, we are currently witnessing a resurrection in the domains of ubiquitous and pervasive systems. These systems and devices heavily rely on efficient wireless communication as an enabler of single and multi-hop wireless data transfers. Awareness of the practical performance capabilities and limitations of existing wireless technologies in various heterogeneous settings is fundamental for the deployment of effective ubiquitous applications. We conduct and analyze an in-depth and detailed single-hop and various multi-hop networking measurements study utilizing a representative set of emerging mobile and IoT devices equipped with a variety of wireless interfaces. Additionally, given the popularity of Android-based D2D systems developed, we propose two solutions that would overcome the inherent two-hop D2D WiFi-Direct limitation, and provide many-hop WiFi-Direct-based communication. Our results provide deep insights on the performance of multi-hop D2D communication within various environments, along with the limitations and trade-offs in the communication technologies, topologies, and solutions adopted.

Following the explosion of Internet-enabled mobile devices and mobile services, users tend to expect network connectivity everywhere they go and at any time. In anticipation of these higher connectivity expectations and requirements especially for vehicular communication, several technologies like WiMAX, cellular networks (i.e., 3G/4G) and wireless mesh networks have been developed. While cellular network communication is either costly or slow, WiMAX which provides data rates comparable to regular WiFi networks, suffers from capacity issues as there are limited channels and hence a limited number of users able to transmit to a single base station. This paper investigates the connectivity challenges in WiFi vehicular opportunistic communications and discusses the utility of such networks at various speeds. We perform a set of simulations to compare state-of-the-art solutions that aim at enhancing the access point entry, production, and exit phases of such communication. We also emulate a best-case solution that enhances most state-of-the-art communication features in order to quantify the potential improvement that can be achieved in vehicular opportunistic networks. The ultimate goal is to provide insights into the true promise and potential of vehicular opportunistic networking.

The pervasiveness of small mobile devices equipped with multiple wireless interfaces enabled novel communication paradigms: opportunistic data transfer between the mobile devices. In this paper, we consider the heterogeneity in forwarding mechanisms as one of the major reasons which affects the performance of forwarding algorithms. Heterogeneity arises in different contexts, for example in device types (i.e., tablets vs. phones vs wearable), operating systems or node objectives. Each node may have its own objectives e.g. security, privacy, trust, battery life, etc. We specifically address the heterogeneity in nodes objectives and propose two different architectures, cooperative and collaborative. In the cooperative mode nodes can only communicate if they share the same forwarding objective such as saving energy or minimizing delays. In the collaborative mode, all nodes can communicate with other nodes after a negotiation phase within which they find objective trade-offs. We then present three different collaborative algorithms and evaluate all proposed algorithms using real mobility traces to find out the message delivery success rate.

With the advent of wearable computing and the resulting growth in mobile application market, we investigate mobile opportunistic cloud computing where mobile devices leverage nearby computational resources in order to save execution time and consumed energy. Our goal is to enable generic computation offloading to heterogeneous devices that include Cloud, mobile devices, and cloudlets. We propose a generic and flexible architecture that maximizes the computation gain with respect to various objective functions such as, minimizing the response time, reducing the overall energy consumption, and increasing the network lifetime. This novel architecture is designed to automate computation offloading to numerous compute resources over disrupted network connections.

The evolution of mobile devices into highly capable computing platforms that sense, store, and execute complex tasks is making them attractive candidates for edge computational micro-cloud settings. Such solutions are creating novel security challenges due to the increased push for more seamless computational cyber-foraging that leverages the exploding proliferation of mobile devices. A major concern is that security challenges stemming from these trends, are growing at a rate exceeding the evolution of security solutions. In this paper, we consider an environment in which computational offloading is performed among a set of mobile devices. We propose HoneyBot, a defense technique for device-to-device (d2d) malicious communication. While classical honeypots designed to isolate distributed denial of service (DDoS) botnet attacks fail to detect d2d insider attacks, HoneyBot nodes detect, track, and isolate such attacks. We propose and investigate detection and tracking algorithms that leverage insecure d2d infected communication channels to accurately and efficiently identify suspect malicious nodes and isolate them. Our data driven evaluation and analysis, based on 3 real world mobility traces, show that the number and placement of HoneyBot nodes (Hb) in the network considerably impact the tracking delay and the detection accuracy.

With the advent of wearable computing and the resulting growth in mobile application market, we investigate mobile opportunistic cloud computing where mobile devices leverage nearby computational resources in order to save execution time and consumed energy. Our goal is to enable generic computation offloading to heterogeneous devices forming a mobile-to-mobile opportunistic computing platform. In this paper, we adopt (1) an analytical approach and (2) an experimental approach to highlight the gain given by mobile-to-mobile opportunistic offloading compared to local execution. We also investigate multiple offloading strategies with regards to both computation time and energy consumption. We propose an auto-splitting and offloading algorithms that computes the optimal chunks sizes that could be offloaded remotely to neighboring mobile device. We show that our splitting and offloading algorithm succeeds in picking the optimal chunk sizes and distribution with up to 99.7% efficiency. In addition, the offloader device saves up to 80% energy while offloading the task remotely. For instance if the offloader device is running out of battery, offloading is the ultimate solution to increase its lifetime.

While WiFi-based indoor localization services are on the rise, existing solutions require periodic updates and therefore exhibit high power demand. In this paper, we propose a novel Social Aware Cluster Based Localization algorithm (SAC-Loc) that leverages social information between nodes in order to dynamically cluster those that exhibit similar mobility patterns. SAC-Loc deploys socially-aware algorithms that dynamically determine when to split and coalesce clusters depending on predicted network topology changes. Based on social ties between encountered nodes, it estimates cluster stability metrics in order to avoid joining crossing nodes with temporary proximity, or the unnecessary splitting of a group due to wireless scanning limitations. We analyze and evaluate our algorithms using a data driven approach based on real-world traces, in addition to an experimental implementation and deployment in our department. While state-of-the-art group localization algorithms can either be energy efficient or highly accurate, SAC-Loc provides a desirable trade-off between accuracy and energy consumption based on popular indoor localization applications.

It is common practice for mobile devices to offload computationally heavy tasks off to a cloud, which has greater computational resources. In this paper, we consider an environment in which computational offloading is made among collaborative mobile devices.We call such an environment a mobile d

Mobile Device Clouds are becoming a reality with the quantitative and qualitative upgrades on mobile devices such as smart-phones and tablets. This proliferation renders mobile devices capable of initiating sophisticated cyberattacks especially when they coordinate together and form a distributed mobile botnets which we call “MobiBots”. MobiBots infect a large number of mobile devices and schedule targeted attacks by leveraging device-to-device (d2d) short range wireless communications. In this work, we first introduce MobiBots, the formation of MobiBots, their challenges, and limitations. We then make the case for MobiBots utilization. We show the potential for and impact of the large scale infection and coordination of mobile devices via short range wireless technologies in attacks against other mobile devices that come within proximity. We show that MobiBots are difficult to detect and isolate compared to common botnets. However, prevention techniques cost at least 40% of the network capacity.

Many mobile applications overcome their device limitations in computational, energy, or data resources by offloading computations to the cloud. In this paper, we consider environments in which computational offloading occurs amongst a set of mobile devices. We call such an environment a mobile device cloud (MDC). In this work, we first highlight the gain in computation time and energy consumption that can be achieved by offloading tasks to nearby devices within an MDC compared to a cloud. We then propose and implement an MDC platform that enables the creation and assessment of various offloading algorithms in MDCs. This platform consists of an Android application deployable across MDC devices, and a test bed to measure power being consumed by a mobile device. We utilize this platform to carry out various offloading experiments on an MDC test bed from which we gain interesting insights into the potential for MDC offloading. Results from these experiments show up to 50% gain in time and 26% gain in energy. Finally, we address the off loadee selection problem in MDCs by proposing several social-based algorithms. The potential promise of this approach is shown by evaluating these algorithms using real data sets that include contact traces and social information of mobile devices in a conference setting.

With the ubiquity of WiFi-enabled smartphones, and large-scale access point deployment, WiFi-based localization is one of the most promising indoor localization systems. Existing WiFi localization solutions, however, exhibit high power demand due to the periodic updates required, which raises the barrier for deployment on mobile devices since battery life is a crucial resource. In this paper, we present an energy efficient architecture, GreenLoc, that leverages the numerous sensors existing on mobile devices, along with typical group mobility patterns, in order to lower the average energy cost for indoor localization. We also propose a cluster-based localization algorithm, integrate it with our GreenLoc architecture, and evaluate its performance via simulation and prototype implementation. Our results show up to 60% reduction in average energy consumed with a small penalty in accuracy that does not impact the performance of the range of applications we target.

Despite the plethora of opportunistic forwarding solutions offered by the research community, we revisit this domain from a new perspective by exploiting the concept of space syntax to enable deployable solutions in large scale urban environments. We present a set of algorithms that build upon space syntax, which predicts natural movement patterns by interacting with pre-built static environments. We design these algorithms for three assumption categories that represent the spectrum of assumptions regarding the underlying environment and node capabilities. We adopt a data-driven approach to evaluate the performance of our algorithms when compared to other state-of-the-art solutions within each representative category that make similar assumptions. Overall, our results show the great promise space syntax based algorithms have for efficiently guiding messages towards the destination. We show 5% to 20% success rate improvement compared to selected well known state-of-the-art forwarding algorithms within each assumption category while reducing the cost in terms of message replicas by up to 10%.

In this paper, we consider an environment in which computational offoading is adopted amongst mobile devices. We call such an environment a mobile device cloud (MDC). In this work, we highlight via emulation, experimenation and real measurements, the potential gain in computation time and energy consumption that can be achieved by offoading tasks within an MDC. We also propose and develop an experimental platform to enable researchers create and experiment with novel offoading algorithms in MDCs.

Social forwarding, recently a hot topic in mobile opportunistic networking, faces extreme challenges from potentially large numbers of mobile nodes, vast areas, and limited communication resources. Such conditions render forwarding more challenging in large-scale networks. We observe that forwarding techniques based on social popularity fail to efficiently forward messages in large scale networks. The social popularity of nodes might not scale with the network size in a way that necessarily correlates with the contact opportunities and mobility patterns of these nodes. In this paper, we demonstrate, based on real mobility traces, the weakness of existing social forwarding algorithms in large scale communities. We address this weakness by proposing strategies for partitioning these large scale communities into sub-communities based on geographic locality or social interests. We also examine exploiting particular nodes, named MultiHomed nodes, in order to disseminate messages across these sub- communities. Finally, we introduce CAF, a Community Aware Forwarding framework, which can easily be integrated with the state-of-the-art social forwarding algorithms in order to improve their performance in large scale networks. We use real mobility traces to evaluate our proposed techniques. Our results empirically show a performance increase of around 40% and 5% to 30% better success delivery rates compared to state-of- the-art social forwarding algorithms, while incurring a marginal increase in cost.

The fundamental challenge in opportunistic networking, regardless of the application, is enabling node cooperation in order to forward a message. While node cooperation is considered as a fundamental property in such networks, ensuring such a property between two devices in mobile opportunistic networks remains largely unexplored. In this paper, we investigate the potential impact of the lack of trust on node cooperation. We adopt a real-trace driven approach to study and analyze the trade-off between trust and success delivery rates in opportunistic networks. We first explore leveraging social information to establish trustworthy communication for mobile opportunistic networks. We then propose six trust based filters that use three social-based estimators of trust including common interests, common friends, and the distance in the social graph, coupled with two major techniques of trust establishment including Relay-to-Relay, and Source-to-Relay. We finally show that our trust filters yield a fair trade-off between trust and success rate by achieving more than 35% success rate compared to an untrusted environment where 10% of the nodes refuse to cooperate in the absence of trust.

The fundamental challenge in opportunistic networking, regardless of the application, is when and how to forward a message. Rank-based forwarding techniques currently represent one of the most promising methods for addressing this message forwarding challenge. While these techniques have demonstrated great efficiency in performance, they do not address the rising concern of fairness amongst various nodes in the network. Higher ranked nodes typically carry the largest burden in delivering messages, which creates a high potential of dissatisfaction amongst them. In this paper, we adopt a real-trace driven approach to study and analyze the tradeoff between the efficiency and fairness of rank-based forwarding techniques in mobile opportunistic networks. Our work comprises three major contributions. First, we quantitatively analyze the tradeoffs between fair and efficient environments. Second, we demonstrate how fairness coupled with efficiency can be achieved based on real mobility traces. Third, we propose FOG, a real-time distributed framework to ensure efficiency-fairness tradeoff using local information. Our data-driven experiment and analysis show that mobile opportunistic communication between users may fail with the absence of fairness in participating high-ranked nodes, and an absolute fair treatment of all users yields inefficient communication performance. Finally our analysis show that FOG ensures relative equality in the distribution of resource usage among neighbor nodes while keeping the success rate and cost performance near optimal.

In opportunistic networks, end-to-end paths between two communicating nodes are rarely available. In such situations, the nodes might still copy and forward messages to nodes that are more likely to meet the destination. The question is which forwarding algorithm offers the best trade off between cost (number of message replicas) and rate of successful message delivery. We address this challenge by developing the PeopleRank approach in which nodes are ranked using a tunable weighted social information. Similar to the PageRank idea, PeopleRank gives higher weight to nodes if they are socially connected to important other nodes of the network. We develop centralized and distributed variants for the computation of PeopleRank. We present an evaluation using real mobility traces of nodes and their social interactions to show that PeopleRank manages to deliver messages with near optimal success rate (close to Epidemic Routing) while reducing the number of message retransmissions by 50% compared to Epidemic Routing.

In opportunistic ad-hoc networks, multi-hop data transfer over contemporaneous paths is unlikely since the devices are often disconnected from each other. However, data can still be stored and forwarded over time in an opportunistic hop-by-hop manner. Previous work has considered how the availability of various types of information such as social relationships can be used to guide forwarding algorithm to make better decisions and bring messages closer to the destination. This implicitly assumes that opportunistic contacts relate with the social property of node. However, the impact of such correlation between social and contact properties on social forwarding performances remains largely unexplored. In this paper we argue that the relevance of such social information (social inputs) is as important as designing a new social forwarding algorithms. We examine multiple datasets to determine the impact of correlation, if any, between social information of individuals and their mobility patterns on the forwarding performances. We propose methods which process the social inputs to improve the relevance of such social information to forwarding. We show that our processing methods could improve the success rate performances of many social forwarding algorithms by more than 30%.

Portable devices have more data storage and increasing communication capabilities everyday. In addition to classic infrastructure based communication, these devices can exploit human mobility and opportunistic contacts to communicate. We analyze the characteristics of such opportunistic forwarding paths. We establish that opportunistic mobile networks in general are characterized by a small diameter, a destination device is reachable using only a small number of relays under tight delay constraint. This property is first demonstrated analytically on a family of mobile networks which follow a random graph process. We then establish a similar result empirically with four data sets capturing human mobility, using a new methodology to efficiently compute all the paths that impact the diameter of an opportunistic mobile networks. We complete our analysis of network diameter by studying the impact of intensity of contact rate and contact duration. This work is, to our knowledge, the first validation that the so called “small world ” phenomenon applies very generally to opportunistic networking between mobile nodes. 1.

Pocket Switched Networking (PSN) is a new communication paradigm between mobile devices. It takes advantage of every local communication opportunity, and the physical mobility of the devices, in order to transport data.