With the rapid growth of Internet of Vehicles (IoV) applications and the advancement of edge computing, resource-limited vehicles (and other IoT devices) increasingly rely on external servers to handle diverse and complex computational tasks. However, this dependence on external servers, which may be malicious or compromised, introduces significant security risks. Replication-based verifiable computing has been proposed as a solution to verify the accuracy of task results, but these approaches are vulnerable to collusion, where compromised servers return identical incorrect results to mislead the vehicle. Existing defenses against collusion either cannot ensure complete protection or become ineffective as the number of colluding servers rises. In this paper, we introduce CoVFeFE, a collusion-resilient verification framework designed to detect and mitigate collusion, even when the majority of servers are compromised. Our framework integrates a rapid detection mechanism that monitors computational conflicts, alongside a heuristic mitigation strategy that identifies and neutralizes colluding servers. Simu-lation results demonstrate that CoVFeFE outperforms existing solutions by successfully identifying all colluding servers, even when they constitute a majority > 50%) of the network.

The rapid advancement of autonomous driving technology is accompanied by substantial challenges, particularly the reliance on remote task execution without ensuring a reliable and accurate returned results. This reliance on external compute servers, which may be malicious or rogue, represents a major security threat. While researchers have been exploring replication-based task verification as a simple, fast, and dependable verifiable computing method to assess the correctness of results, colluding malicious workers can easily defeat this method. Existing collusion detection and mitigation solutions often require the use of a trusted third party server or verified tasks which may be hard to guarantee, or solutions that assume the presence of a minority of colluding servers. We propose SERENE, a collusion resilient replication-based verification framework that detects, and mitigates colluding workers. Unlike state-of-the-art solutions, SERENE uses a lightweight detection algorithm that detects collusion based on a small set of verification tasks. Mitigation requires a two stage process to group the workers and identifying colluding from honest workers. We implement and compare SERENE's performance to Staab et. al, resulting in an average of 50% and 60% accuracy improvement in detection and mitigation accuracy respectively.

The rapid growth of data-driven applications has prompted a shift towards Information-Centric Networking (ICN) in the Internet landscape. Like TCP/IP's routing tables, ICN employs Forward Information Base (FIB) tables. However, unlike IP addresses, the URL-like naming scheme in ICN can cause FIB tables to grow exponentially, leading to delays in prefix lookups. Current solutions use computationally intensive explicit FIB aggregation method, or on-demand routing schemes, which use a discovery mechanism to help reduce the number of FIB records and thus have shorter lookup times, rely on flooding-based mechanisms and building routes for all requests, introducing further scalability challenges. In this paper, we propose SAMBA, an Approximate Forwarding-based Self Learning, that uses the nearest FIB trie record to the given prefix for reducing the number of discoveries thus keeping the FIB table small. By choosing the nearest prefix to a given name prefix, SAMBA uses Implicit Prefix Aggregation (IPA) which implicitly aggregates the FIB records and reduces the number of Self Learning discoveries required. Coupled with the approximate forwarding, SAMBA can achieve efficient and scalable forwarding. We demonstrate that SAMBA can help reduce lookup times by up to 45%. SAMBA also implements multipath discovery and consumer-controlled flooding mechanisms, which help minimize networking overhead. Our simulation results show that SAMBA reduces the FIB table size twenty fold compared to traditional Self Learning schemes.

While the success of edge and fog computing increased with the proliferation of the Internet of Things (IoT) solutions, such novel computing paradigm, that moves compute resources closer to the source of data and services, must address many challenges such as reducing communication overhead to/from datacenters, the latency to compute and receive results, as well as energy consumption at the mobile and IoT devices. fog-to-fog (f2f) cooperation has recently been proposed to increase the computation capacity at the network edge through cooperation across multiple stakeholders. In this paper we adopt an analytical approach to studying f2f cooperation paradigm. We highlight the benefits of using such new paradigm in comparison with traditional three-tier fog computing paradigms. We use a Continuous Time Markov Chain (CTMC) model for the N f2f cooperating nodes and cast cooperation as an optimization problem, which we solve using the proposed model.

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.

Multi-Agent Reinforcement Learning (MARL) is nowadays widely used to solve real-world and complex decisions in various domains. While MARL can be categorized into independent and cooperative approaches, we consider the independent approach as a simple, more scalable, and less costly method for large-scale distributed systems, such as network packet forwarding. In this paper, we quantitatively and qualitatively assess the benefits of leveraging such independent agents learning approach, in particular IQL-based algorithm, for packet forwarding in computer networking, using the Named Data Networking (NDN) architecture as a driving example. We put multiple IQL-based forwarding strategies (IDQF) to the test and compare their performances against very basic forwarding schemes and simple topologies/traffic models to highlight major challenges and issues. We discuss the main issues related to the poor performance of IDQF and quantify the impact of these issues on isolation when training and testing the IDQF models under different model tuning parameters and network topologies/characteristics.

New breed of applications, such as autonomous driving and their need for computation-aided quick decision making has motivated the delegation of compute-intensive services (e.g., video analytic) to the more powerful surrogate machines at the network edge–edge computing (EC). Recently, the notion of pervasive edge computing (PEC) has emerged, in which users’ devices can join the pool of the computing resources that perform edge computing. Inclusion of users’ devices increases the computing capability at the edge (adding to the infrastructure servers), but in comparison to the conventional edge ecosystems, it also introduces new challenges, such as service orchestration (i.e., service placement, discovery, and migration). We propose uDiscover, a novel user-driven service discovery and utilization framework for the PEC ecosystem. In designing uDiscover, we considered the Named-Data Networking architecture for balancing users workloads and reducing user-perceived latency. We propose proactive and reactive service discovery approaches and assess their performance in PEC and infrastructure-only ecosystems. Our simulation results show that (i) the PEC ecosystem reduces the user-perceived delays by up to 70%, and (ii) uDiscover selects the most suitable server–"accurate" delay estimates with less than 10% error–to execute any given task.

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 50x lower task completion times leveraging its network-based compute reuse mechanism compared to cases, where reuse is not available.

Named Data Networking (NDN) architectural features, including multicast data delivery, stateful forwarding, and in-network data caching, have shown promise for applications such as video streaming and file sharing. However, collaborative applications, requiring a multi-producer participation introduce new NDN design challenges. In this paper, we highlight these challenges in the context of the Network Time Protocol (NTP) and one of its most widely-used deployments for NTP server discovery, the NTP pool project. We discuss the design requirements for the support of NTP and NTP pool and present general directions for the design of a time synchronization protocol over NDN, coined Named Data Networking Time Protocol (NDNTP).

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.

Delay-sensitive applications have been driving the move away from cloud computing, which cannot meet their low-latency requirements. Edge computing and programmable switches have been among the first steps toward pushing computation closer to end-users in order to reduce cost, latency, and overall resource utilization. This article presents the "compute-less" paradigm, which builds on top of the well known edge computing paradigm through a set of communication and computation optimization mechanisms (e.g.,, in-network computing, task clustering and aggregation, computation reuse). The main objective of the compute-less paradigm is to reduce the migration of computation and the usage of network and computing resources, while maintaining high Quality of Experience for end-users. We discuss the new perspectives, challenges, limitations, and opportunities of this compute-less paradigm.

This paper highlights the benefits of information-centric networking (ICN) and its named data networking (NDN) architecture for future edge computing applications in a multi-tenant multi-stakeholder ecosystem. We consider an exemplary scenario of autonomous driving to discuss open issues for efficient and timely distributed compute-centric networking. We discuss what NDN has to offer for efficient and resilient edge computing, and what needs to be done to augment NDN to move from an information-centric architecture to a compute-centric architecture. Based on the autonomous driving scenario, we identify security, naming, networking challenges, and discuss the different options to tackle these challenges and propose potential solutions.

The technological advancements along with the proliferation of smart and connected devices (things) motivated the exploration of the creation of smart cities aimed at improving the quality of life, economic growth, and efficient resource utilization. Some recent initiatives defined a smart city network as the interconnection of the existing independent and heterogeneous networks and the infrastructure. However, considering the heterogeneity of the devices, communication technologies, network protocols, and platforms the interoperability of these networks is a challenge requiring more attention. In this paper, we propose the design of a novel Information-Centric Smart City architecture (iSmart), focusing on the demand of the future applications, such as efficient machineto-machine communication, low latency computation offloading, large data communication requirements, and advanced security. In designing iSmart, we use the Named-Data Networking (NDN) architecture as the underlying communication substrate to promote semantics-based communication and achieve seamless compute/data sharing.

Online social media is being widely used by social scientists to study human behavior. Researchers have explored different feature extraction (FE) and classification techniques to perform sentiment analysis, topic identification, etc. Most studies tend to evaluate FE and classification methods using only one particular class of datasets---well-defined with little/no noise or with well-defined noise. For instance, when the datasets under study have different noise characteristics, various FE and/or classification methods may fail to identify a given topic. In this paper, we fill this gap by quantitatively comparing multiple FE methods and classifiers using three different datasets (two moderator-controlled blogs and one single-authored personal blogs) related to Autism Spectrum Disorder (ASD). Our result shows that no particular combination of FE and classifier is the best overall, but choosing the right ones can improve accuracy by over 30%.

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.

Edge computing has been proposed as a solution to the ultra-low latency requirements of novel compute-intensive applications and systems such as autonomous driving, smart cities, etc. However, the scale of such compute-intensive systems coupled with stricter application requirements causes the computation load to exceed the current rate at which we provision the resources at the edge. In this paper, we advocate that reusing (partly or fully) the results of already executed computational tasks among multiple users to calculate new results, which we call "compute reuse", has the potential to significantly reduce resource utilization and lessen the time needed for the completion (i.e., execution) of new tasks. We conduct an experimental study to quantify the performance gain and improved utilization of resources that stem from systems being able to reuse previous computations. Our results demonstrate that such systems can result in up to 50× lower completion times and substantially lower utilization of computing resources. We also discuss alternative design options and tradeoffs of building systems aware of compute reuse semantics.

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.