Remote Direct Memory Access (RDMA) is increasingly used as a shared communication substrate across datacenter workloads with very different scheduling needs, from request-response services and storage fan-out to AI training collectives. Proper request scheduling can reduce communication time, but in practice, no RDMA flow scheduling is enabled in datacenters, leaving traffic to simple fair sharing. We present STORM, a NIC-level scheduler for all types of RDMA workloads using NIC-only information: the known RDMA request size, and per-queue-pair backlog. STORM converts these signals into a small number of extra priority levels on the wire and prioritizes requests that are either near completion or blocking queued dependent work. STORM requires no application hints and works with both in-order RoCEv2 and newer RDMA stacks that tolerate reordering. We prototype STORM on an FPGA NIC with negligible overhead. Across representative cloud and LLM training workloads, STORM reduces training iteration time by up to 12% and reduces average and P99 flow completion slowdown by up to 90% compared to fair scheduling.

Traditionally, Internet of Things (IoT) communication technologies have been designed to offer low bit rates (from ∼10 2 to ∼10 6 bit/s). However, recent IoT-intended technologies like 5G Reduced Capability (RedCap) support significantly greater bit rates (up to ∼10 8 bit/s), enabling emerging IoT use cases that demand greater capacity. Thus, the spectrum of IoT scenarios and corresponding requirements is expanding, a trend which is expected to continue with 6G networks. In this context, support, configuration and performance of a crucial upper-layer protocol like TCP become challenging. In this paper, based on our IETF standardization work, we describe how TCP can run suitably on a wide variety of IoT environments (from highly constrained scenarios to resource-rich ones). Furthermore, we present and study the novel TCP option called TCP Acknowledgment Rate Request (TARR), designed for further TCP adaptability, which is particularly useful for current and future IoT networks.

Centralised architecture models are often adopted in the development of smart cities to take advantage of the technologies provided by dominant companies such as Google, Meta, and Amazon. This approach often comes with significant drawbacks, such as (i) application developers becoming dependent on the policies and rules imposed by large companies, which are subject to change, and (ii) users having their data collected and used haphazardly, and often without authorisation, which can undermine the trust that users place in the solution. Infrastructure for smart cities should, therefore, incorporate decentralised technologies that satisfy or reinforce important quality attributes such as trustworthiness, privacy, and security. The main objective of this article is to survey and discuss how the services of a smart city can be implemented using decentralised architectures; we explain the advantages and disadvantages of decentralised architectures and emerging technologies. We review the specialised literature and compile a set of emerging technologies that are essential to conceive a decentralised architectural solution for smart cities. The current state-of-the-art in decentralisation shows that this model can be implemented with current technologies and help to satisfy the quality attributes mentioned above, and potentially reinforce the degree of trustworthiness of the solution. We also propose a four-dimension guideline that functions as a conceptual driver for structuring actions for researchers, system architects, policymakers, and city authorities in the design, evaluation, and long-term maintenance of decentralised smart cities, thereby enhancing their capacity to address escalating urban challenges. We conclude that the set of technologies discussed in this article has the potential to be reused and replicated across smart cities. • Choice between C-architecture and D-architecture for smart cities. • Concerns about interoperability, privacy, vendor lock-in, and sustainability. • Decentralisation offers improved interoperability, privacy, and sustainability. • Approach to decentralised digital identity, permissions, and integration solutions.

Recent attacks on federated learning (FL) can introduce malicious model updates that can circumvent widely adopted Euclidean distance-based detection methods. This article proposes a novel defense strategy, referred to as LayerCAM-AE, designed to counteract model poisoning in FL. The LayerCAM-AE puts forth a new Layer Class Activation Mapping (LayerCAM) integrated with an autoencoder (AE), significantly enhancing detection capabilities. Specifically, LayerCAM-AE generates a heat map for each local model update, which is then transformed into a more compact visual explanation. The autoencoder processes the LayerCAM heat maps from the local model updates, improving their distinctiveness and increasing the accuracy in spotting anomalous maps and malicious local models. To mitigate the risk of misclassifications in LayerCAM-AE, a voting algorithm is developed, where a local model update is flagged as malicious if its heat maps are consistently suspicious over several communication rounds. Extensive tests on the SVHN and CIFAR-100 datasets are performed under both Independent and Identically Distributed (IID) and non-IID settings in comparison with the state-of-the-art ResNet-50 and REGNETY-800MF defense models. The experimental results show that LayerCAM-AE increases detection rates (Recall: 1.0, Precision: 1.0, FPR: 0.0, Accuracy: 1.0, F1 score: 1.0, AUC: 1.0) and the test accuracy of FL, surpassing both the ResNet-50 and REGNETY-800MF. Our code is available at: https://github.com/jjzgeeks/LayerCAM-AE .

Edge computing enables distributed machine learning models to be deployed and trained near the user space. However, the intricate nature of edge computing raises several challenges to distributed machine learning frameworks: 1) inferior convergence arising from non-independent and identically distributed (non-IID) edge data; 2) inefficient structural adaptation, where device dynamism complicates the adjustment of aggregation structure; and 3) reduced training efficiency, as resource heterogeneity and fluctuations create systemic stragglers. To address these issues, a distributed hierarchical model training framework has been proposed by considering the dynamic aggregation structure and frequency in this paper. This framework designs an Edge Aggregation Structure and Frequency method, namely EASF, for distributed model training in heterogeneous edge computing environments. First, a dynamic distributed aggregation structure method is formulated to consider various data distribution patterns. This method constructs and modifies the aggregation structure in a distributed manner to adapt to variations in working edge devices. Second, a self-adapted aggregation frequency method and a timeout abandonment mechanism are proposed to allow each node to update its aggregation frequency adaptively. Lastly, a theoretical analysis demonstrates the convergence property of the EASF method in dynamic environments. Extensive experiments have been conducted on a set of open testbeds. Results show that the EASF significantly improves the efficiency and accuracy of hierarchical model training in heterogeneous edge computing.

This article proposes a new cyberattack on decentralized federated learning (DFL), named user isolation poisoning (UIP). While following the standard DFL protocol of receiving and aggregating benign local models, a malicious user strategically generates and distributes compromised updates to undermine the learning process. The objective of the new UIP attack is to diminish the impact of benign users by isolating their model updates, thereby manipulating the shared model to reduce the learning accuracy. To realize this attack, we design a novel threat model that leverages an adversarial message-passing graph (MPG) neural network. Through iterative message passing, the adversarial MPG progressively refines the representations (also known as embeddings or hidden states) of each benign local model update. By orchestrating feature exchanges among connected nodes in a targeted manner, the malicious users effectively curtail the genuine data features of benign local models, thereby diminishing their overall influence within the DFL process. The MPG-based UIP attack is implemented in PyTorch, demonstrating that it effectively reduces the test accuracy of DFL by 49.5% and successfully evades existing cosine similarity- and Euclidean distance-based defense strategies.

In this paper, we propose a modularized framework for communication processes applicable to crash and Byzantine fault-tolerant consensus protocols. We abstract basic communication components and show that the communication process of the classic consensus protocols such as RAFT, single-decree Paxos, PBFT, and Hotstuff, can be represented by the combination of communication components. Based on the proposed framework, we develop an approach to analyze the consensus reliability of different protocols, where link loss and node failure are measured as a probability. We propose two latency optimization methods and implement a RAFT system to verify our theoretical analysis and the effectiveness of the proposed latency optimization methods. We also discuss decreasing consensus failure rate by adjusting protocol designs. This paper provides theoretical guidance for the design of future consensus systems with a low consensus failure rate and latency under the possible communication loss.

In recent years, the maturation of emerging technologies such as Virtual Reality, Digital Twins and Blockchain has accelerated the realization of the metaverse. As a virtual world independent of the real world, the metaverse will provide users with a variety of virtual activities which bring great convenience to society. In addition, the metaverse can facilitate digital twins, which offers transformative possibilities for the industry. Thus, the metaverse has attracted the attention of the industry, and a huge amount of capital is about to be invested. However, the development of the metaverse is still in its infancy and little research has been undertaken so far. We describe the development of the metaverse. Next, we introduce the architecture of the socially beneficial metaverse (SB-Metaverse) and we focus on the technologies that support the operation of SB-Metaverse. In addition, we also present the applications of SB-Metaverse. Finally, we discuss several challenges faced by SB-Metaverse which must be addressed in the future.

The metaverse is a shared virtual 3D space that combines immersive experiences with applications in gaming, social interactions, commerce, and more. It is rapidly becoming a reality, driven by advances in virtual reality, augmented reality, artificial intelligence, blockchain, and other emerging technologies. Among these, blockchain technology enables secure and decentralized ownership as well as seamless interoperability of virtual assets. Non-fungible tokens ensure verifiable ownership and fraud prevention, while smart contracts facilitate automated peer-to-peer transactions. Blockchain’s security and transparency promote trust and innovation, laying the foundation for a connected and user-driven metaverse ecosystem. In this paper, we explore the role of blockchain technology as a key enabler for the metaverse, providing solutions for decentralization, governance through decentralized autonomous organizations, interoperable mechanisms, digital asset ownership, traceability, auditing, and identity management. We present the key difference between traditional virtual worlds and the metaverse, and why blockchain is preferred over other decentralized technologies for the metaverse. We comprehensively review recent advances in metaverse system architectures, focusing on state-of-the-art solutions and lessons learned. We compare the existing literature based on key parameters; namely, contributions, advantages, limitations, and applications. We present key challenges, including deepfake threats, identity theft and brand infringement risks, mental health risks, digital safety and gambling risks, virtual world laws and regulations, and privacy and data security concerns. We outline future recommendations for enabling a sustainable and user-friendly metaverse ecosystem.

This paper proposes a new visual explanation-based defense mechanism, namely, FedCAMAE, against model poisoning attacks on federated learning (FL), which integrates Layer Class Activation Mapping (LayerCAM) and autoencoder to offer a scientifically more powerful detection capability compared to existing Euclidean distance-based or machine learning-based approaches. Specially, FedCAMAE generates a fine-grained heat map assisted by Layer-CAM for each uploaded local model update, transforming each local model update into a lower-dimensional, visual representation. To accentuate the hidden features of the heat maps, autoencoder is seamlessly embedded into the proposed FedCAMAE, which can refine the the heat maps and enhance their distinguishability, thereby increasing the success rate of identifying anomalous heat maps and malicious local models. We test ResNet-50 and REGNETY-800MF deep learning models with SVHN and CIFAR-100 datasets under Non-Independent and Identically Distributed (Non-IID) setting, respectively. The results demonstrate that Fed-CAMAE offers superior test accuracy of FL global model compared to the state-of-the-art methods. Our code is available at: https://github.com/jjzgeeks/LayerCAM-AE

Vehicular services aim to provide smart and timely services (e.g., collision warning) by taking the advantage of recent advances in artificial intelligence and employing task offloading techniques in mobile edge computing. In practice, the volume of vehicles in the Internet of Vehicles (IoV) often surges at a single location and renders the edge servers (ESs) severely overloaded, resulting in a very high delay in delivering the services. Therefore, it is of practical importance and urgency to coordinate the resources of ESs with bandwidth allocation for mitigating the occurrence of a spike traffic flow. For this challenge, existing work sought the periodicities of traffic flow by analyzing historical traffic data. However, the changes in traffic flow caused by sudden traffic conditions cannot be obtained from these periodicities. In this paper, we propose a distributed traffic flow forecasting and task offloading approach named TFFTO to optimize the execution time and power consumption in service processing. Specifically, graph attention networks (GATs) are leveraged to forecast future traffic flow in short-term and the traffic volume is utilized to estimate the number of services offloaded to the ESs in the subsequent period. With the estimate, the current load of the ESs is adjusted to ensure that the services can be handled in a timely manner. Potential game theory is adopted to determine the optimal service offloading strategy. Extensive experiments are conducted to evaluate our approach and the results validate our robust performance.

The existing Internet architecture lacks support for naming locations and resolving them to the myriad addressing mechanisms we use beyond IP. We propose the Spatial Name System (SNS) that allows for the assignment of hierarchical location-based names and for resolution schemes that are both global and local. Since we extend the DNS, our scheme allows for the integration of spatial names into existing applications and opens up new possibilities for sensor networks and augmented reality.

We study a cache network in which intermediate nodes equipped with caches can serve requests. We model the problem of jointly optimizing caching and routing decisions with link capacity constraints over an arbitrary network topology. This problem can be formulated as a continuous diminishing-returns (DR) submodular maximization problem under multiple continuous DR-supermodular constraints, and is NP-hard. We propose a poly-time alternating primal-dual heuristic algorithm, in which primal steps produce solutions within 1 - approximation factor from the optimal. Through extensive experiments, we demonstrate that our proposed algorithm significantly out-performs competitors.

The centralized system becomes less efficient, secure, and resilient as the network size and heterogeneity increase due to its inherent single point of failure issues. Distributed consensus mechanisms characterized by decentralization, autonomy, parallelism, and fault-tolerance can meet the increasing demands of safety and security in critical interconnected systems. This article establishes a Node and Link probabilistic failure model in the presence of node and communication link failures for a representative crash fault-tolerant distributed consensus protocol: RAFT. The analytical results in terms of the probability density function and the mean value of consensus reliability are derived. Two important reliability performance indicators, Reliability Gain and Tolerance Gain are proposed to indicate the linear relationship between the consensus reliability and two basic parameters, i.e., the joint failure rate and the maximum number of tolerant faulty nodes, which provide the theoretical guidance for quickly deploying an RAFT system. The special case of a distributed consensus network with already a certain number of failures and its adverse impact are evaluated. The Markov probabilistic models, definitions of Reliability Gain and Tolerance Gain, and the analysis methods proposed in this article can be extended to other consensus mechanisms.

Wireless jammer activity from malicious or malfunctioning devices cause significant disruption to mobile network services and user QoE degradation. In practice, detection of such activity is manually intensive and costly, taking days and weeks after the jammer activation to detect it. We present a novel data-driven jammer detection framework termed JADE that leverages continually collected operator-side cell-level KPIs to automate this process. As part of this framework, we develop two deep learning based semi-supervised anomaly detection methods tailored for the jammer detection use case. JADE features further innovations, including an adaptive thresholding mechanism and transfer learning based training to efficiently scale JADE for operation in real-world mobile networks. Using a real-world 4G RAN dataset from a multinational mobile network operator, we demonstrate the efficacy of proposed jammer detection methods relative to commonly used anomaly detection methods. We also demonstrate the robustness of our proposed methods in accurately detecting jammer activity across multiple frequency bands and diverse types of jammers. We present real-world validation results from applying our methods in the operator’s network for online jammer detection. We also present promising results on pinpointing jammer locations when our methods spot jammer activity in the network along with cell site location data.

In this paper, we aim to propose a novel transmission control protocol (TCP) congestion control method from a cross-layer-based perspective and present a deep reinforcement learning (DRL)-driven method called DRL-3R (DRL for congestion control with Radio access network information and Reward Redistribution) so as to learn the TCP congestion control policy in a superior manner. In particular, we incorporate the RAN information to timely grasp the dynamics of RAN, and empower DRL to learn from the delayed RAN information feedback potentially induced by several consecutive actions. Meanwhile, we relax the implicit assumption (that the feedback to one specific action returns at a round-trip-time (RTT) after the action is applied) in previous researches, by redistributing the rewards and evaluating the merits of actions more accurately. Experiment results show that besides maintaining a reasonable fairness, DRL-3R significantly outperforms classical congestion control methods (e.g., TCP Reno, Westwood, Cubic, BBR and DRL-CC) on network utility by achieving a higher throughput while reducing delay in various network environments.

We have recently witnessed a number of new software-defined paradigms of VANET in what is referred to as software-defined vehicle networks (SDVN). In order to evaluate the performance of these new proposals and architectures, analytical and simulation models are needed. In this paper, we propose an analytical model based on ON-OFF queueing networks under exponential and general service time distributions. The model can be used to evaluate the performance of SDVNs and takes into account the effect of mobility such as, hand overs, node turning ON/OFF, node going temporary out of coverage, and intermittent connections. This mobility effect was modelled as a queueing station with exponentially random ON-OFF service times, where traffic arrives according to a Poisson random process during the exponentially random ON period and the service time is exponentially distributed. However, during the OFF period the service time is exponentially distributed but with lower rates. We studied the ON-OFF queueing behaviour extensively for both finite-capacity and infinite-capacity queues. Three hypothetical SDVN scenarios were considered, taking into account the effect of mobility and the large number of connected nodes. Results were cross-validated with those obtained by a simulation model. These tools will be valuable for researchers interested in getting quantitative answers for their SDVN architectures.

With the massive amount of data generated from mobile devices and the increase of computing power of edge devices, the paradigm of Federated Learning has attracted great momentum. In federated learning, distributed and heterogeneous nodes collaborate to learn model parameters. However, while providing benefits such as privacy by design and reduced latency, the heterogeneous network present challenges to the synchronisation methods, or barrier control methods, used in training, regarding system progress and model convergence etc. The design of these barrier mechanisms is critical for the performance and scalability of federated learning systems. We propose a new barrier control technique called Probabilistic Synchronous Parallel (PSP). In contrast to existing mechanisms, it introduces a sampling primitive that composes with existing barrier control mechanisms to produce a family of mechanisms with improved convergence speed and scalability. Our proposal is supported with a convergence analysis of PSP-based SGD algorithm. In practice, we also propose heuristic techniques that further improve the efficiency of PSP. We evaluate the performance of proposed methods using the federated learning specific FEMNSIT dataset. The evaluation results show that PSP can effectively achieve good balance between system efficiency and model accuracy, mitigating the challenge of heterogeneity in federated learning.

IoT-driven smart societies are modern service-networked ecosystems, whose proper functioning is hugely based on the success of supply chain relationships. Robust security is still a big challenge in such ecosystems, catalyzed primarily by naive cyber-security practices (e.g., setting default IoT device passwords) on behalf of the ecosystem managers, i.e., users and organizations. This has recently led to some catastrophic malware-driven DDoS and ransomware attacks (e.g., the Mirai and WannaCry attacks). Consequently, markets for commercial third-party cyber-risk management (CRM) services (e.g., cyber-insurance) are steadily but sluggishly gaining traction with the rapid increase of IoT deployment in society, and provides a channel for ecosystem managers to transfer residual cyber-risk post attack events. Current empirical studies have shown that such residual cyber-risks affecting smart societies are often heavy-tailed in nature and exhibit tail dependencies. This is both, a major concern for a profit-minded CRM firm that might normally need to cover multiple such dependent cyber-risks from different sectors (e.g., manufacturing and energy) in a service-networked ecosystem, and a good intuition behind the sluggish market growth of CRM products. In this article, we provide: 1) a rigorous general theory to elicit conditions on (tail-dependent) heavy-tailed cyber-risk distributions under which a risk management firm might find it (non)sustainable to provide aggregate cyber-risk coverage services for smart societies and 2) a real-data-driven numerical study to validate claims made in theory assuming boundedly rational cyber-risk managers, alongside providing ideas to boost markets that aggregate dependent cyber-risks with heavy-tails. To the best of our knowledge, this is the only complete general theory till date on the feasibility of aggregate CRM.

As authors of our recently accepted article:Aggregate Cyber-Risk Management in the IoT Age: Cautionary Statistics for (Re)Insurers and Likes, published in the IEEE IoT Journal, we regret that we have found a few errors in the numerical evaluation setup of the works in[1]and[2]that we had borrowed for our accepted paper. In this correction statement, we describe the errors in detail, correct it, and present our revised results with a renewed experimental setup, hoping it to replace the existing incorrect numerical results in the accepted paper. We apologize for the inconvenience caused to the reader. We emphasize that the numerical evaluation section does not in any way hamper the theoretical contributions in this article, and was initially only meant to provide some empirical evidence for whether the theory proposed in this article generalizes to behavioral settings introduced in[2].

Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis proximity to where data are captured based on artificial intelligence. Edge intelligence aims at enhancing data processing and protects the privacy and security of the data and users. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this article, we present a thorough and comprehensive survey of the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, i.e., edge caching, edge training, edge inference, and edge offloading based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare, and analyze the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, and so on. This article provides a comprehensive survey of edge intelligence and its application areas. In addition, we summarize the development of the emerging research fields and the current state of the art and discuss the important open issues and possible theoretical and technical directions.

Mobile crowdsensing (MCS) represents a new sensing paradigm that utilizes the smart mobile devices to collect and share data. Traditional MCS systems mainly leverages the people carried smartphones and other wearable devices which are constrained by the limited sensing capability and battery power. With the popularity of unmanned vehicles like unmanned aerial vehicles (UAVs) and driverless cars, they can provide much more reliable, accurate and cost-efficient sensing services due to to their equipped more powerful sensors. In this paper, we propose a distributed control framework for energy-efficient and DIstributed VEhicle navigation with chaRging sTations, called “e-Divert”. It is a distributed multi-agent deep reinforcement learning (DRL) solution, which uses a convolutional neural network (CNN) to extract useful spatial features as the input to the actor-critic network to produce a real-time action. Also, e-Divert incorporates a distributed prioritized experience replay for better exploration and exploitation, and a long short-term memory (LSTM) enabled N-step temporal sequence modeling module. The solution fully explores the spatiotemporal nature of the considered scenario for better vehicle cooperation and competition between themselves and charging stations, to maximize the energy efficiency, data collection ratio, geographic fairness, and minimize the energy consumption simultaneously. Through extensive simulations, we find an appropriate set of hyperparameters that achieve the best performance, i.e., 5 actors in Ape-X architecture, priority exponent 0.5, and LSTM sequence length 3. Finally, we compare with four baselines including one state-of-the-art approach MADDPG. Results show that our proposed e-Divert significantly improves the energy efficiency, as compared to MADDPG, by 3.62 and 2.36 times on average when varying different numbers of vehicles and charging stations, respectively.

In-app advertising is a multi-billion dollar industry that is an essential part of the current digital ecosystem, and is amenable to sensitive consumer information often being sold downstream without the knowledge of consumers, and in many cases to their annoyance. While this practice, in cases, may result in long-term benefits for the consumers, it can result in serious information privacy (IP) breaches of very significant impact (e.g., breach of genetic data) in the short term. The question we raise through this article is: does the type of information being traded downstream play a role in the degree of IP risks generated? We investigate two general (one-many) information trading market structures between a single data aggregating seller (e.g., enterprise app) and multiple competing buyers (e.g., ad-networks, retailers), distinguished by mutually exclusive and privacy sanitized aggregated consumer data (information) types: (i) data entailing strategically complementary actions among buyers and (ii) data entailing strategically substituting actions among buyers. Our primary question of interest here is: trading which type of data might pose less information privacy risks for society? To this end, we show that at market equilibrium IP trading markets exhibiting strategic substitutes between buying firms pose lesser risks for IP in society, primarily because the `substitutes' setting, in contrast to the `complements' setting, economically incentivizes appropriate consumer data distortion by the seller in addition to restricting the proportion of buyers to which it sells. Moreover, we also show that irrespective of the data type traded by the seller, the likelihood of improved IP in society is higher if there is purposeful or free-riding based transfer/leakage of data between buying firms. This is because the seller finds itself economically incentivized to restrict the release of sanitized consumer data with respect to the span of its buyer space, as well as in improved data quality.

In this paper we propose SCDP, a general-purpose data transport protocol for data centres that, in contrast to all other protocols proposed to date, supports efficient one-to-many and many-to-one communication, which is extremely common in modern data centres. SCDP does so without compromising on efficiency for short and long unicast flows. SCDP achieves this by integrating RaptorQ codes with receiver-driven data transport, packet trimming and Multi-Level Feedback Queuing (MLFQ); (1) RaptorQ codes enable efficient one-to-many and many-to-one data transport; (2) on top of RaptorQ codes, receiver-driven flow control, in combination with in-network packet trimming, enable efficient usage of network resources as well as multi-path transport and packet spraying for all transport modes. Incast and Outcast are eliminated; (3) the systematic nature of RaptorQ codes, in combination with MLFQ, enable fast, decoding-free completion of short flows. We extensively evaluate SCDP in a wide range of simulated scenarios with realistic data centre workloads. For one-to-many and many-to-one transport sessions, SCDP performs significantly better compared to NDP and PIAS. For short and long unicast flows, SCDP performs equally well or better compared to NDP and PIAS.

Multi-agent system (MAS) needs to mobilize multiple simple agents to complete complex tasks. However, it is difficult to coherently coordinate distributed agents by means of limited local information. In this demo, we propose a decentralized collaboration method named as "stigmergy" in network-assisted MAS, by exploiting digital pheromones (DP) as an indirect medium of communication and utilizing deep reinforcement learning (DRL) on top. Correspondingly, we implement an experimental platform, where KHEPERA IV robots form targeted specific shapes in a decentralized manner. Experimental results demonstrate the effectiveness and efficiency of the proposed method. Our platform could be conveniently extended to investigate the impact of network factors (e.g., latency, data rate, etc).

We present a federated, asynchronous, and $(\varepsilon, \delta)$-differentially private algorithm for $\PCA$ in the memory-limited setting. % Our algorithm incrementally computes local model updates using a streaming procedure and adaptively estimates its $r$ leading principal components when only $\mathcal{O}(dr)$ memory is available with $d$ being the dimensionality of the data. % We guarantee differential privacy via an input-perturbation scheme in which the covariance matrix of a dataset $\B{X} \in \R^{d \times n}$ is perturbed with a non-symmetric random Gaussian matrix with variance in $\mathcal{O}\left(\left(\frac{d}{n}\right)^2 \log d \right)$, thus improving upon the state-of-the-art. % Furthermore, contrary to previous federated or distributed algorithms for $\PCA$, our algorithm is also invariant to permutations in the incoming data, which provides robustness against straggler or failed nodes. % Numerical simulations show that, while using limited-memory, our algorithm exhibits performance that closely matches or outperforms traditional non-federated algorithms, and in the absence of communication latency, it exhibits attractive horizontal scalability.

Summary Decentralization, in the form of mesh networking and blockchain, two promising technologies, is coming to the telecommunications industry. Mesh networking allows wider low‐cost Internet access with infrastructures built from routers contributed by diverse owners, whereas blockchain enables transparency and accountability for investments, revenue, or other forms of economic compensations from sharing of network traffic, content, and services. Crowdsourcing network coverage, combined with crowdfunding costs, can create economically sustainable yet decentralized Internet access. This means that every participant can invest in resources and pay or be paid for usage to recover the costs of network devices and maintenance. While mesh networks and mesh routing protocols enable self‐organized networks that expand organically, cryptocurrencies and smart contracts enable the economic coordination among network providers and consumers. We explore and evaluate two existing blockchain software stacks, Hyperledger Fabric (HLF) and Ethereum geth with Proof of Authority (PoA) intended as a local lightweight distributed ledger, deployed in a real city‐wide production mesh network and in laboratory network. We quantify the performance and bottlenecks and identify the current limitations and opportunities for improvement to serve locally the needs of wireless mesh networks, without the privacy and economic cost of relying on public blockchains.

Low-power wide-area networks (LPWANs) are experiencing high momentum as an inexpensive solution for enabling the Internet-of-Things (IoT) applications. Recent Internet connectivity support developments are expected to further fuel the adoption of LPWAN. However, the latter present challenges to the Internet protocols. Remarkably, many LPWAN scenarios exhibit a multimodal round-trip time (RTT) distribution, which deviates from the common Internet RTT characteristics. This leads to a significant mismatch between retransmission timeout (RTO), computed by the standard transmission control protocol (TCP) or alternative experimental RTO algorithms, and RTT. In this article, we present the Multimodal RTO algorithm, which is able to self-adapt to the current RTT mode and produce suitable RTO values. The evaluation results show that the Multimodal RTO reduces the RTO versus RTT misalignment of the TCP RTO algorithm by an average factor of up to 5, and reduces latency in the presence of losses by up to 2 orders of magnitude, while operating safely. The Multimodal RTO is currently being considered by the IETF as a candidate mechanism for standardization.

Modern malware is complex, stealthy, and employ anti-forensics techniques to evade detection. In order to detect malware, data must be collected, such, allows further analyses of the malware's behaviour. However, when both the malware and the detecting system run on the same domain (the CPU) it's questionable whether the data acquired by the acquisition method is not tampered with. Hardware based techniques, such as acquiring data out-of-band using a PCIe device allow for data acquisition that is deemed trusted when the acquisition method does not rely on any data present on the host memory. Unfortunately, in Input-Output Memory Management Unit (IOMMU) based systems, peripheral devices access to host memory go through a stage of translation by the IOMMU. The translation tables which reside in the host's memory are subject to malware control, hence are not trustworthy. In this paper we present a method that allows acquiring the data reliably without dependant on data residing in host memory, even when IOMMU is being used to restrict devices. We show how accessing host physical memory is achieved and discuss why this is not a vulnerability in some platforms, but rather a powerful tool for securing data acquisition when the host is not trusted to perform the acquisition.

Community networks (CNs) have gained momentum in the last few years with the increasing number of spontaneously deployed WiFi hotspots and home networks. These networks, owned and managed by volunteers, offer various services to their members and to the public. While Internet access is the most popular service, the provision of services of local interest within the network is enabled by the emerging technology of CN micro-clouds. By putting services closer to users, micro-clouds pursue not only a better service performance, but also a low entry barrier for the deployment of mainstream Internet services within the CN. Unfortunately, the provisioning of these services is not so simple. Due to the large and irregular topology, high software and hardware diversity of CNs, a “careful” placement of micro-clouds services over the network is required to optimize service performance. This paper proposes to leverage state information about the network to inform service placement decisions, and to do so through a fast heuristic algorithm, which is critical to quickly react to changing conditions. To evaluate its performance, we compare our heuristic with one based on random placement in Guifi.net, the biggest CN worldwide. Our experimental results show that our heuristic consistently outperforms random placement by 2x in bandwidth gain. We quantify the benefits of our heuristic on a real live video-streaming service, and demonstrate that video chunk losses decrease significantly, attaining a 37% decrease in the packet loss rate. Further, using a popular Web 2.0 service, we demonstrate that the client response times decrease up to an order of magnitude when using our heuristic. Since these improvements translate in the QoE (Quality of Experience) perceived by the user, our results are relevant for contributing to higher QoE, a crucial parameter for using services from volunteer-based systems and adapting CN micro-clouds as an eco-system for service deployment.

In this paper, we propose to leverage the emerging deep learning techniques for sequential modeling of user interests based on big social data, which takes into account influence of their social circles. First, we present a preliminary analysis for two popular big datasets from Yelp and Epinions. We show statistically sequential actions of all users and their friends, and discover both temporal autocorrelation and social influence on decision making, which motivates our design. Then, we present a novel hybrid deep learning model, Social-Aware Long Short-Term Memory (SA-LSTM), for predicting the types of item/PoIs that a user will likely buy/visit next, which features stacked LSTMs for sequential modeling and an autoencoder-based deep model for social influence modeling. Moreover, we show that SA-LSTM supports end-to-end training. We conducted extensive experiments for performance evaluation using the two real datasets from Yelp and Epinions. The experimental results show that (1) the proposed deep model significantly improves prediction accuracy compared to widely used baseline methods; (2) the proposed social influence model works effectively; and (3) going deep does help improve prediction accuracy but a not-so-deep deep structure leads to the best performance.

Unikernel specializes a minimalistic LibOS and a target application into a standalone single-purpose virtual machine (VM) running on a hypervisor, which is referred to as (virtual) appliance . Compared to traditional VMs, Unikernel appliances have smaller memory footprint and lower overhead while guaranteeing the same level of isolation. On the downside, Unikernel strips off the process abstraction from its monolithic appliance and thus sacrifices flexibility, efficiency, and applicability. In this article, we examine whether there is a balance embracing the best of both Unikernel appliances (strong isolation) and processes (high flexibility/efficiency). We present KylinX, a dynamic library operating system for simplified and efficient cloud virtualization by providing the pVM (process-like VM) abstraction. A pVM takes the hypervisor as an OS and the Unikernel appliance as a process allowing both page-level and library-level dynamic mapping. At the page level, KylinX supports pVM fork plus a set of API for inter-pVM communication (IpC, which is compatible with conventional UNIX IPC). At the library level, KylinX supports shared libraries to be linked to a Unikernel appliance at runtime. KylinX enforces mapping restrictions against potential threats. We implement a prototype of KylinX by modifying MiniOS and Xen tools. Extensive experimental results show that KylinX achieves similar performance both in micro benchmarks (fork, IpC, library update, etc.) and in applications (Redis, web server, and DNS server) compared to conventional processes, while retaining the strong isolation benefit of VMs/Unikernels.

Recent research has shown a substantial active presence of bots in online social networks (OSNs). In this article, we perform a comparative analysis of the usage and impact of bots and humans on Twitter—one of the largest OSNs in the world. We collect a large-scale Twitter dataset and define various metrics based on tweet metadata. Using a human annotation task, we assign “bot” and “human” ground-truth labels to the dataset and compare the annotations against an online bot detection tool for evaluation. We then ask a series of questions to discern important behavioural characteristics of bots and humans using metrics within and among four popularity groups. From the comparative analysis, we draw clear differences and interesting similarities between the two entities.

Emerging Machine Learning (ML) techniques, such as Deep Neural Network, are widely used in today's applications and services. However, with social awareness of privacy and personal data rapidly rising, it becomes a pressing and challenging societal issue to both keep personal data private and benefit from the data analytics power of ML techniques at the same time. In this paper, we argue that to avoid those costs, reduce latency in data processing, and minimise the raw data revealed to service providers, many future AI and ML services could be deployed on users' devices at the Internet edge rather than putting everything on the cloud. Moving ML-based data analytics from cloud to edge devices brings a series of challenges. We make three contributions in this paper. First, besides the widely discussed resource limitation on edge devices, we further identify two other challenges that are not yet recognised in existing literature: lack of suitable models for users, and difficulties in deploying services for users. Second, we present preliminary work of the first systematic solution, i.e. Zoo, to fully support the construction, composing, and deployment of ML models on edge and local devices. Third, in the deployment example, ML service are proved to be easy to compose and deploy with Zoo. Evaluation shows its superior performance compared with state-of-art deep learning platforms and Google ML services.

Edge computing is shaping the way to run services in the Internet by allowing the computations to become available close to the user's proximity. Many implementations have been recently proposed to facilitate the service delivery in data centers and distributed networks. However, we argue that those implementations cannot fully support the operations in Community Mesh Networks (CMNs) since the network connection is highly intermittent and unreliable. In this paper, we propose PiCasso, a novel multi-access edge computing platform that combines the advances in lightweight virtualisation and Information-Centric Networking (ICN). PiCasso utilises in-network caching and name based routing of ICN to optimise the forwarding path of service delivery. We analyse the data collected from Guifi.net, the biggest CMN worldwide, to develop smart heuristic for the service deployment. Through a real deployment in Guifi.net, we show that our service deployment heuristic HANET (HArdware and NETwork Resources), improves the response time up to 53% and 28.7% for stateless and stateful services respectively. Finally, using PiCasso for the service delivery in Guifi.net, we achieve 43% traffic reduction compared to the traditional host-centric communication.

The current Internet infrastructure, initially conceived to provide closed group connectivity to a limited user-base, is now facing the challenges of catering to over three billion users with dynamically changing data, transport, and access requirements. Content-Centric Networking (CCN) is being explored as a possible future Internet architecture. Named-Data Networking (NDN) exemplifies CCN and with the ever-changing security landscape, it is imperative to build it with intrinsic resilience based on the security by design narrative. In this extended abstract, we explore the fake interest fiooding by adversaries to launch a Distributed Denial of Service (DDoS) attack in NDN. We propose Kiram as an intelligent anomaly recognition and alert generation mechanism serving as a collaborative countermeasure to a DDoS attack. The alert generated by Kiram is named Warding Off Evil (WOE). This poster presents results establishing the effectiveness of Kiram based on substantial simulations over a fairly realistic topology.

The world is driven by the Internet and there is no doubt about its importance in our daily life. However, the Internet has rarely been upgraded since its advent, although the ISO OSI model has already provided the required flexibility. With innovations being blocked, the Internet is suffering from a high-degree of ossification (e.g., the slow progress of IPv6 update), leading to suboptimal efficiency for emerging applications as well as enlarged maintenance cost.In this paper, we present VirtualStack, which aims at bringing back the interchangeability of network layers. VirtualStack is based on the idea of protocol virtualization, where the most suitable protocol stack can be dynamically composed and applied on the fly according to the characteristics of both the application and the physical link. Through a comprehensive study, we show that many existing but not widely deployed protocols outperform the omnipresent TCP under various link technologies and network conditions. This provides the necessary insight for dynamic composition of the network protocol stack. We further evaluate VirtualStack under a typical Internet setting with multiple hops under different conditions. The experimental results confirm the benefits as well as the potential of VirtualStack.

Machine Learning (ML) techniques have begun to dominate data analytics applications and services. Recommendation systems are the driving force of online service providers such as Amazon. Finance analytics has quickly adopted ML to harness large volume of data in such areas as fraud detection and risk-management. Deep Neural Network (DNN) is the technology behind voice-based personal assistance, self-driving cars [1], image processing [3], etc. Many popular data analytics are deployed on cloud computing infrastructures. However, they require aggregating users’ data at central server for processing. This architecture is prone to issues such as increased service response latency, communication cost, single point failure, and data privacy concerns.

Scoped-flooding is used for content discovery in a broad networking context and it has significant impact on the design of caching algorithms in a communication network. Despite its wide usage, a thorough analysis on how scoped-flooding affects a network's performance, e.g., caching and content discovery efficiency, is missing. To develop a better understanding, we first model the behavior of scoped-flooding by the help of a theoretical model on network growth and utility. Next, we investigate the effects of scoped-flooding on various topologies in information-centric networks (ICNs). Using the proposed ring model, we show that flooding can be constrained within a small neighborhood to achieve most of the gains which come from areas with relatively low growth rate, i.e., the network edge. We also study two flooding strategies and compare their behaviors. Given that caching schemes favor more popular items in competition for cache space, popular items are expected to be stored in diverse parts of the network compared to the less popular items. We propose to exploit the resulting divergence in availability along with the routers' topological properties to fine tune the flooding radius. Our results shed light on designing both efficient content discovery mechanism and effective caching algorithms for future ICN.

Recent research has shown a substantial active presence of bots in online social networks (OSNs). In this paper we utilise our previous work (Stweeler) to comparatively analyse the usage and impact of bots and humans on Twitter, one of the largest OSNs in the world. We collect a large-scale Twitter dataset and define various metrics based on tweet metadata. Using a human annotation task we assign 'bot' and 'human' ground-truth labels to the dataset, and compare the annotations against an online bot detection tool for evaluation. We then ask a series of questions to discern important behavioural characteristics of bots and humans using metrics within and among four popularity groups. From the comparative analysis we draw differences and interesting similarities between the two entities, thus paving the way for reliable classification of bots, and studying automated political infiltration and advertisement campaigns.

Online social networks (OSNs) have seen a remarkable rise in the presence of surreptitious automated accounts. Massive human user-base and business-supportive operating model of social networks (such as Twitter) facilitates the creation of automated agents. In this paper we outline a systematic methodology and train a classifier to categorise Twitter accounts into 'automated' and 'human' users. To improve classification accuracy we employ a set of novel steps. First, we divide the dataset into four popularity bands to compensate for differences in types of accounts. Second, we create a large ground truth dataset using human annotations and extract relevant features from raw tweets. To judge accuracy of the procedure we calculate agreement among human annotators as well as with a bot detection research tool. We then apply a Random Forests classifier that achieves an accuracy close to human agreement. Finally, as a concluding step we perform tests to measure the efficacy of our results.

The success of Wi-Fi technology as an efficient and low-cost last-mile access solution has enabled massive spontaneous deployments generating storms of beacons all across the globe. Emerging location systems are using these beacons to observe mobility patterns of people through portable or wearable devices and offer use-cases that can help solve critical problems in the developing world. In this paper, we design and develop a novel prototype to organise these spontaneous deployments of Access Points into what we call virtual cells (vcells). We compute virtual cells from a list of Access Points collected from different active scans for a geographical region. We argue that virtual cells can be encoded using Bloom filters to implement the location process. Lastly, we present two illustrative use-cases to showcase the suitability and challenges of the technique.

The long-heralded Internet of Things (IoT) is finally becoming a reality. From factories and the ubiquitous Internet-connected fridge, we now see heating control systems, cars, dishwashers, and all manner of common-place devices being connected. While this has certainly realized new capabilities, such as the ability to control one’s domestic heating remotely, the benefits are perhaps more mixed: every device that we can remotely control is a device that someone else can remotely hack. And that is just the devices—the literal things; in tandem we also see increasing intrusion of Internet-connectivity into services, practices and everyday infrastructures such as transport and retail. Coupled with the increasingly invasive deployment of these devices into everyday lives, the result is a substantial increase in threats to privacy arising from the IoT.

Information-centric networking (ICN) is a popular research topic. At its heart is the concept of in-network caching. Various algorithms have been proposed for optimizing ICN caching, many of which rely on collaborative principles, i.e. multiple caches interacting to decide what to store. Past work has assumed altruistic nodes that will sacrifice their own performance for the global optimum. We argue that this assumption is insufficient and oversimplifies the reality. We address this problem by modeling the in-network caching problem as a Nash bargaining game. We develop optimal and heuristic caching solutions that consider both performance and fairness. We argue that only algorithms that are fair to all parties involved in caching will encourage engagement and cooperation. Through extensive simulations, we show our heuristic solution, FairCache, ensures that all collaborative caches achieve performance gains without undermining the performance of others.

An unexpected increase in natural disasters has prompted a large interest in governments and organisations to utilise ICT for many different purposes such as preparation, impact mitigation, loss reduction and relief efforts. This paper presents initial work on studying disaster scenarios from device level perspective to characterise network infrastructural behaviour during extraordinary situations. We find connectivity challenges during disasters and observe sharp decline of quality metrics and loss of station quantity between ordinary and extraordinary time periods. We also make distinctions between usual and unusual behaviour seen during ordinary and extraordinary situations.

Novel network systems need to be carefully evaluated before their actual deployments in developing regions. However, large-scale network experiment is a challenging task. Simulations, emulations, and real-world testbeds all have their advantages and disadvantages. In this paper we present LiteLab, a light-weight platform specialized for large-scale networking experiments. We cover in detail its design, key features, and architecture. We also perform an extensive evaluation of Lite-Lab's performance and accuracy and show that it is able to both simulate network parameters with high accuracy, and also able to scale up to very large networks. LiteLab is flexible, easy to deploy, and allows researchers to perform large-scale network experiments with a short development cycle. We have used LiteLab for many different kinds of network experiments and are planning to make it available for others to use as well.

There are many more things with moving parts in the world than computers. These are the objects that are being connected, initially artefacts, but also the natural world. They are connected both by being sensed, and via actuators. For a true Internet of things to emerge with all its potential value for innovation in efficiencies, the sensors and actuators must actually be reachable from anywhere, anytime, just like computers on today's internet. And they must be locally and remotely programmable. Of course, there must be mechanisms to implement policies about access and use. However, these policies are complex, since they don't merely reflect informational rules, but also rules about the physical world - a car may be restricted to certain speeds in certain areas, but also to different speeds and areas at different times, due to the driver. Unfortunately, in the rush to instrument and control the world of things, the complexity of the world seems to have been forgotten. Worse, the typical system software being deployed in many places does not reflect the last few decades evolution of safety and security work that has gone in to the implementation of operating systems and protocols. All too often, we here another system uses an embedded OS with no isolation or a protocol stack with known vulnerabilities, or is shipped with default access control credentials to millions of customers. This is not good enough. In this talk, I will cover some of the work we've been doing in the Microsoft sponsored project in Cambridge and QMUL, on the technical and legal challenges that are now facing our community.

Caching is a core principle of information-centric networking (ICN). Many novel algorithms have been proposed for enabling ICN caching, many of which rely on collaborative principles, i.e. multiple caches interacting to decide what to store. Past work has assumed entirely altruistic nodes that will sacrifice their own performance for the global optimum. In this paper, we argue that this assumption is flawed. We address this problem by modelling the in-network caching problem as a Nash bargaining game. We develop optimal and heuristic caching solutions that explicitly consider both performance and fairness. We argue that only algorithms that are fair to all parties will encourage engagement and cooperation. Through extensive simulations, we show our heuristic solution, FairCache, ensures that all collaborative caches achieve performance gains without undermining the performance of others.

In this paper, we present a novel idea of multipath multicast, which is imperative to bandwidth intensive applications, in the context of multimedia streaming. In addition to congestion control, multipath TCP (MPTCP) has been proposed to establish multiple paths in a network to improve network reliability. Application-layer multicast (ALM) has been proposed to leverage end systems instead of dedicated routers to multicast that is important for an easy large-scale deployment as compared to IP-based multicast. This paper presents our novel idea of multipath multicast in the form of a simple experimental framework called MP-ALM in which we combine the multiplicity feature of MPTCP with the application-layer multicast (ALM). We extensively simulate MP-ALM using ns-3 and use iPerf to generate streaming multicast-MPTCP traffic. Simulation results show that MP-ALM can be beneficial for a better user experience and reduced overall network congestion in the perspective of multicast multimedia streaming.

The last 5 years have seen a dramatic shift in media distribution. For decades, TV and radio were solely provisioned using push-based broadcast technologies, forcing people to adhere to fixed schedules. The introduction of catch-up services, however, has now augmented such delivery with online pull-based alternatives. Typically, these allow users to fetch content for a limited period after initial broadcast, allowing users flexibility in accessing content. Whereas previous work has investigated both of these technologies, this paper explores and contrasts them, focusing on the network consequences of moving towards this multifaceted delivery model. Using traces from nearly 6 million users of BBC iPlayer, one of the largest catch-up TV services, we study this shift from push- to pull-based access. We propose a novel technique for unifying both push- and pull-based delivery: the Speculative Content Offloading and Recording Engine (SCORE). SCORE operates as a set-top box, which interacts with both broadcast push and online pull services. Whenever users wish to access media, it automatically switches between these distribution mechanisms in an attempt to optimize energy efficiency and network resource utilization. SCORE also can predict user viewing patterns, automatically recording certain shows from the broadcast interface. Evaluations using our BBC iPlayer traces show that, based on parameter settings, an oracle with complete knowledge of user consumption can save nearly 77% of the energy, and over 90% of the peak bandwidth, of pure IP streaming. Optimizing for energy consumption, SCORE can recover nearly half of both traffic and energy savings.

Opinions from people can either be biased or reflect low participation due to legitimate concerns about privacy and anonymity. To alleviate those concerns, the identity of a message sender should be disassociated from the message while the contents of the actual message should be hidden from any relaying nodes. We propose a novel message routing scheme based on probabilistic forwarding that guarantees message privacy and sender anonymity through additively homomorphic public-key encryption. Our scheme is applicable to anonymous surveys and microblogging.

Recommender systems have changed the way people originally find products, information, and even their social circles. However, most existing research activities neglect its time-varying feature, i.e., the growing input data, the change of user behaviors. In order to sustain the high accuracy of recommendations, systems have to be updated regularly. However, the more often the update proceeds, the more cost of time and other computational resources. Thus, it is critical to strike the balance between accuracy and cost. In this paper, we propose an adaptive recommender system by using feedback control frameworks. The proposed solution continuously monitors its changes and estimates the loss of performance (in terms of accuracy) from two perspectives: data problem(data aging and data deficient) in training set, and changes of user behavior by “revisiting ratio”. When the benefit of performing an update exceeds the cost of resources, the system update itself. Theoretical analysis and extensive results by using a real data set are supplemented to show the advantages of the proposed system.

This demonstration presents a novel software defined platform for achieving collaborative and energy-aware WiFi offloading. The platform consists of an extensible central controller, programmable offloading agents, and offloading extensions on mobile devices. Driven by our extensive measurements of energy consumption on smartphones, we propose an effective energy-aware offloading algorithm and integrate it to our platform. By enabling collaboration between wireless networks and mobile users, our solution can make optimal offloading decisions that improve offloading efficiency for network operators and achieve energy saving for mobile users. To enhance deployability, we have released our platform under open-source licenses on GitHub.

Distributed consensus is fundamental in distributed systems for achieving fault-tolerance. The Paxos algorithm has long dominated this domain, although it has been recently challenged by algorithms such as Raft and Viewstamped Replication Revisited. These algorithms rely on Paxos's original assumptions, unfortunately these assumptions are now at odds with the reality of the modern internet. Our insight is that current consensus algorithms have significant availability issues when deployed outside the well defined context of the datacenter. To illustrate this problem, we developed Coracle, a tool for evaluating distributed consensus algorithms in settings that more accurately represent realistic deployments. We have used Coracle to test two examples of network configurations that contradict the liveness claims of the Raft algorithm. Through the process of exercising these algorithms under more realistic assumptions, we demonstrate wider availability issues faced by consensus algorithms when deployed on real world networks.

In this paper, we study the problem of efficient data recovery using the data mules approach, where a set of mobile sensors with advanced mobility capabilities re-acquire lost data by visiting the neighbors of failed sensors, thereby improving network resiliency. Our approach involves defining the optimal communication graph and mules' placements such that the overall traveling time and distance is minimized regardless to which sensors crashed. We explore this problem under different practical network topologies such as general graphs, grids and random linear networks and provide approximation algorithms based on multiple combinatorial techniques. Simulation experiments demonstrate that our algorithms outperform various competitive solutions for different network models, and that they are applicable for practical scenarios.

editorial Free Access Share on Introduction to: Special Issue on Smartphone-Based Interactive Technologies, Systems, and Applications Editors: James She Hong Kong University of Science & Technology, Hong Kong Hong Kong University of Science & Technology, Hong KongView Profile , Alvin Chin BMW Group, United States BMW Group, United StatesView Profile , Feng Xia Dalian University of Technology, China Dalian University of Technology, ChinaView Profile , Jon Crowcroft University of Cambridge, UK University of Cambridge, UKView Profile Authors Info & Claims ACM Transactions on Multimedia Computing, Communications, and ApplicationsVolume 12Issue 1sArticle No.: 11pp 1–4https://doi.org/10.1145/2820398Published:21 October 2015Publication History 4citation201DownloadsMetricsTotal Citations4Total Downloads201Last 12 Months18Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

Due to their importance to society, communication networks should be built and operated to withstand failures. However, cost considerations make network providers less inclined to take robustness measures against failures that are unlikely to manifest, like several failures coinciding simultaneously in different geographic regions of their network. Considering networks embedded in a two-dimensional plane, we study the problem of finding a critical region-a part of the network that can be enclosed by a given elementary figure of predetermined size-whose destruction would lead to the highest network disruption. We determine that only a polynomial, in the input, number of nontrivial positions for such a figure needs to be considered and propose a corresponding polynomial-time algorithm. In addition, we consider region-aware network augmentation to decrease the impact of a regional failure. We subsequently address the region-disjoint paths problem, which asks for two paths with minimum total weight between a source (s) and a destination (d) that cannot both be cut by a single regional failure of diameter D (unless that failure includes s or d). We prove that deciding whether region-disjoint paths exist is NP-hard and propose a heuristic region-disjoint paths algorithm.

In this paper we consider the problem of efficient data gathering in sensor networks for arbitrary sensor node deployments. The efficiency of the solution is measured by a number of criteria: total energy consumption, total transport capacity, latency and quality of the transmissions. We present a number of different constructions with various tradeoffs between aforementioned parameters. We provide theoretical performance analysis for our approaches, present their distributed implementation and discuss the different aspects of using each. We show that in many cases our output-sensitive approximation solution performs better than the currently known best results for sensor networks. Our simulation results validate the theoretical findings.

This demo presents how phone functionality can be offloaded from a smartphone over wireless link to a PhoneLet by sharing one SIM card across multiple devices. This can lead to significant cost and network load reductions by decreasing the number of simultaneously connected mobile clients. Furthermore, it can save energy for the mobile user when connected to a powered PhoneLet by offloading phone functionality. It absorbs the energy cost of online presence and inefficient mobile applications' communication patterns, instead providing connectivity for the user over a WiFi link.

The fast increase of mobile traffic from smartphone-like devices has created a huge pressure for the cellular operators to manage the network infrastructure and resources. Offloading the mobile traffic to alternative networks such as WiFi is sought as a promising direction to solve this problem cost-effectively. According to our study and experimental findings, existing research proposals are lack of concern for the complexity of network deployment and device limitations, which impedes the solution deployment. To overcome such challenge, we propose SoftOffload, a programmable framework for collaborative mobile traffic offloading. SoftOffload takes the advantage of software defined networking (SDN) paradigm in terms of openness and extensibility. We have implemented the first prototype utilising the open source Floodlight platform.

To bring their innovative ideas to market, those embarking in new ventures have to raise money, and, to do so, they have often resorted to banks and venture capitalists. Nowadays, they have an additional option: that of crowdfunding. The name refers to the idea that funds come from a network of people on the Internet who are passionate about supporting others' projects. One of the most popular crowdfunding sites is Kickstarter. In it, creators post descriptions of their projects and advertise them on social media sites (mainly Twitter), while investors look for projects to support. The most common reason for project failure is the inability of founders to connect with a sufficient number of investors, and that is mainly because hitherto there has not been any automatic way of matching creators and investors. We thus set out to propose different ways of recommending investors found on Twitter for specific Kickstarter projects. We do so by conducting hypothesis-driven analyses of pledging behavior and translate the corresponding findings into different recommendation strategies. The best strategy achieves, on average, 84% of accuracy in predicting a list of potential investors' Twitter accounts for any given project. Our findings also produced key insights about the whys and wherefores of investors deciding to support innovative efforts.

The trend of replacing public static signages with digital displays creates opportunities for interactive display systems, which can be used in collaborative workspaces, social gaming platforms and advertising. Based on marketing communication concepts and existing models for consumer behavior, three stages, namely attraction, interaction and conation, are defined in this article to analyze the effectiveness of interactive display advertising. By reviewing various methods and strategies employed by existing systems with attraction, interaction and conation stages, this article concludes that smart mobile devices should be integrated as a component to increase the effectiveness of interactive displays as advertising tools. Future research challenges related to this topic are also discussed.

We present unikernels, a new approach to deploying cloud services via applications written in high-level source code. Unikernels are single-purpose appliances that are compile-time specialised into standalone kernels, and sealed against modification when deployed to a cloud platform. In return they offer significant reduction in image sizes, improved efficiency and security, and should reduce operational costs. Our Mirage prototype compiles OCaml code into unikernels that run on commodity clouds and offer an order of magnitude reduction in code size without significant performance penalty. The architecture combines static type-safety with a single address-space layout that can be made immutable via a hypervisor extension. Mirage contributes a suite of type-safe protocol libraries, and our results demonstrate that the hypervisor is a platform that overcomes the hardware compatibility issues that have made past library operating systems impractical to deploy in the real-world.

Mobile phones in the 3G/4G era enable us to stay connected not only to the voice network, but also to online services like social networks. In this paper, we study the energy and network costs of mobile applications that provide continuous online presence (e.g. WhatsApp, Facebook, Skype). By combining measurements taken on the mobile and the cellular access network, we reveal a detailed picture of the mechanisms selected to implement online presence, along with their effect on handset energy consumption and network signaling traffic.

Datacenter networking has brought high-performance storage systems' research to the foreground once again. Many modern storage systems are built with commodity hardware and TCP/IP networking to save costs. In this paper, we highlight a group of problems that are present in such storage systems and which are all related to the use of TCP. As an alternative, we explore Trevi: a fountain coding-based approach for distributing I/O requests that overcomes these problems while still efficiently scheduling resources across both networking and storage layers. We also discuss how receiver-driven flow and congestion control, in combination with fountain coding, can guide the design of Trevi and provide a viable alternative to TCP for datacenter storage.

The popularity of smartphones, cloud computing, and the app store model have led to cellular networks being used in a completely different way than what they were designed for. As a consequence, mobile applications impose new challenges in the design and efficient configuration of constrained networks to maximize application's performance. Such difficulties are largely caused by the lack of cross-layer under- standing of interactions between different entities -applications, devices, the network and its management plane. In this paper, we describe RILAnalyzer, an open-source tool that provides mechanisms to perform network analysis from within a mobile device. RILAnalyzer is capable of recording low-level radio information and accurate cellular net- work control-plane data, as well as user-plane data. We demonstrate how such data can be used to identify previously overlooked issues. Through a small user study across four cellular network providers in two European countries we infer how different network configurations are in reality and explore how such configurations interact with application logic, causing network and energy overheads.

"Catch-up", or on-demand access of previously broadcast TV content over the public Internet, constitutes a significant fraction of peak time network traffic. This paper analyses consumption patterns of nearly 6 million users of a nationwide deployment of a catch-up TV service, to understand the network support required. We find that catch-up has certain natural scaling properties compared to traditional TV: The on-demand nature spreads load over time, and users have much higher completion rates for content streams than previously reported. Users exhibit strong preferences for serialised content, and for specific genres.

Planners and social psychologists have suggested that the recognizability of the urban environment is linked to people's socio-economic well-being. We build a web game that puts the recognizability of London's streets to the test. It follows as closely as possible one experiment done by Stanley Milgram in 1972. The game picks up random locations from Google Street View and tests users to see if they can judge the location in terms of closest subway station, borough, or region. Each participant dedicates only few minutes to the task (as opposed to 90 minutes in Milgram's). We collect data from 2,255 participants (one order of magnitude a larger sample) and build a recognizability map of London based on their responses. We find that some boroughs have little cognitive representation; that recognizability of an area is explained partly by its exposure to Flickr and Foursquare users and mostly by its exposure to subway passengers; and that areas with low recognizability do not fare any worse on the economic indicators of income, education, and employment, but they do significantly suffer from social problems of housing deprivation, poor living conditions, and crime. These results could not have been produced without analyzing life off- and online: that is, without considering the interactions between urban places in the physical world and their virtual presence on platforms such as Flickr and Foursquare. This line of work is at the crossroad of two emerging themes in computing research - a crossroad where "web science" meets the "smart city" agenda.

In this paper, two non-linear complexity measures, namely approximate entropy and sample entropy are investigated as feature extraction methods for evaluating the regularity of the epileptic EEG signals. Furthermore, in order to obtain more efficient feature extraction for EEG signals, an optimized algorithm for sample entropy measure (O-SampEn) is proposed which removes the calculation redundancy and optimizes the computation procedure for sample entropy measure. Clinical EEG data was obtained from 20 intracranial electrodes placed within the epileptogenic zone in five epilepsy patients during both interictal and ictal periods. In terms of the experimental results, both sample entropy and approximate entropy analysis show lower values during epileptic seizures, which mean an increase of EEG signal regularity during ictal state. Compared with approximate entropy, the feature extraction based on sample entropy measure is more sensitive to EEG signal variety caused by epileptic seizures, approximately 10.14%~20.02% higher than the results using approximate entropy. In addition, the proposed optimized algorithm for sample entropy can run 9.52~36.16 times faster than the original sample entropy algorithm according to the simulation. High discrimination ability and fast computation speed of the proposed optimized sample entropy algorithm demonstrate its huge potential as a novel feature extraction method for real-time epileptic seizure detection.

Policy makers are calling for new socio-economic measures that reflect subjective well-being, to complement traditional measures of material welfare as the Gross Domestic Product (GDP). Self-reporting has been found to be reasonably accurate in measuring one's well-being and conveniently tallies with sentiment expressed on social media (e.g., those satisfied with life use more positive than negative words in their Facebook status updates). Social media content can thus be used to track well-being of individuals. A question left unexplored is whether such content can be used to track well-being of entire physical communities as well. To this end, we consider Twitter users based in a variety of London census communities, and study the relationship between sentiment expressed in tweets and community socio-economic well-being. We find that the two are highly correlated: the higher the normalized sentiment score of a community's tweets, the higher the community's socio-economic well-being. This suggests that monitoring tweets is an effective way of tracking community well-being too.

We study the relationship between Facebook popularity (number of contacts) and personality traits on a large number of subjects. We test to which extent two prevalent viewpoints hold. That is, popular users (those with many social contacts) are the ones whose personality traits either predict many offline (real world) friends or predict propensity to maintain superficial relationships. We find that the predictor for number of friends in the real world (Extraversion) is also a predictor for number of Facebook contacts. We then test whether people who have many social contacts on Facebook are the ones who are able to adapt themselves to new forms of communication, present themselves in likable ways, and have propensity to maintain superficial relationships. We show that there is no statistical evidence to support such a conjecture.

Mobile phones and tablets can be considered as the first incarnation of the post-PC era. Their explosive adoption rate has been driven by a number of factors, with the most signifcant influence being applications (apps) and app markets. Individuals and organizations are able to develop and publish apps, and the most popular form of monetization is mobile advertising.

Social-networking sites have started to offer tools that suggest "guests" who should be invited to user-defined social events (e.g., birthday parties, networking events). The problem of how to recommend people to events is similar to the more traditional (recommender system) problem of how to recommend events (items) to people (users). Yet, upon Foursquare data of "who visits what" in the city of London, we show that a state-of-the-art recommender system does not perform well -mainly because of data sparsity. To fix this problem, we add domain knowledge to the recommendation process. From the complex system literature in human mobility, we learn two insights: 1) there are special individuals (often called power users) who visit many places; and 2) individuals go to a venue not only because they like it but also because they are close-by. We model these insights into two simple models and learn that: 1) simply recommending power users works better than random but is far from producing the best recommendations; 2) an item-based recommender system produces accurate recommendations; and 3) recommending places that are closest to a user's geographic center of interest produces recommendations that are as accurate as, if not more accurate than, item-based recommender's. This last result has practical implications as it offers guidelines for designing location-based recommender systems and for partly addressing cold-start situations.

This demo presents Signposts, a system to provide users with a secure, simple mechanism to establish and maintain communication channels between their personal cloud of named devices. Signpost names exist in the DNSSEC hierarchy, and resolve to secure end-points when accessed by existing DNS clients. Signpost clients intercept user connection intentions while adding privacy and multipath support. Signpost servers co-ordinate clients to dynamically discover routes and overcome the middleboxes that pervade modern edge networks. The demo will show a simple scenario where an individual's personal devices (phone, laptop) are interconnected via Signposts while sitting on different networks behind various middleboxes. As a result they will be able to fetch and push data between each other, demonstrated by, e.g., simple web browsing, even as the network configuration changes.

A variety of personal backup services now allow users to synchronise their files across multiple devices such as laptops and smartphones. These applications typically operate by synchronising each device with a centralised storage service across the Internet. However, access to the Internet may occasionally not be available, leaving any unsynchronised content in a vulnerable state. To address this, applications could alternatively make use of storage capacity provided by other devices within close proximity, using ad-hoc or local network connectivity. Such devices can provide a secondary storage tier in case of Internet connectivity issues, and could also be used to forward files to central storage at a later time. In our proposed design, we delegate the task of propagating information across locally networked devices to a lower layer, by making use of a content-centric opportunistic network platform (Haggle). This allows our application, Mistify, to treat the neighbourhood of peers as a single distributed content repository (or “mist”), in a manner similar to the way in which existing applications interface with the cloud. Mistify employs a differentiated replication strategy, with the aim of improving the safety of items in the mist. In our evaluation of the prototype, we have found that in a simulated network of locally-connected peers, the prototype was able to achieve a high level of availability for stored content, without resorting to flooding. Furthermore, Mistify was able to deliver a high proportion of content to the cloud, even when only a small proportion of nodes were given Internet connectivity.

Increases in the number of TV channels requires users to spend more time to select their preferred channels since the user interaction for browsing is practically limited to the conventional remote control with a two-way scrolling button. We formally define the problem to construct the optimal channel ordering which minimizes the seek distance in selecting channels and show this problem is NP-hard. In addition, we present a reasonable heuristic to solve this problem. The proposed method constructs an efficient channel ordering by applying a hierarchical clustering algorithm based on the frequencies of switching events between channels. We demonstrate the feasibility of this method by applying a number of well-known hierarchical clustering algorithms and evaluating the number of user inputs required for selecting channels. Our experimental results show that the proposed method significantly decreases the number of user inputs compared with the conventional methods.

The seven papers in this special section focus om recent advances in the growing research field of television.

Nowadays companies increasingly aggregate location data from different sources on the Internet to offer location-based services such as estimating current road traffic conditions, and finding the best nightlife locations in a city. However, these services have also caused outcries over privacy issues. As the volume of location data being aggregated expands, the comfort of sharing one's whereabouts with the public at large will unavoidably decrease. Existing ways of aggregating location data in the privacy literature are largely centralized in that they rely on a trusted location-based service. Instead, we propose a piece of software (SpotMe) that can run on a mobile phone and is able to estimate the number of people in geographic locations in a privacy-preserving way: accurate estimations are made possible in the presence of privacy-conscious users who report, in addition to their actual locations, a very large number of erroneous locations. The erroneous locations are selected by a randomized response algorithm. We evaluate the accuracy of SpotMe in estimating the number of people upon two very different realistic mobility traces: the mobility of vehicles in urban, suburban and rural areas, and the mobility of subway train passengers in Greater London. We find that erroneous locations have little effect on the estimations (in both traces, the error is below 18% for a situation in which more than 99% of the locations are erroneous), yet they guarantee that users cannot be localized with high probability. Also, the computational and storage overheads for a mobile phone running Spot Me are negligible, and the communication overhead is limited.

In this paper, we investigate the ability of using Delay Tolerant Networking (DTN) in Public Safety networks, where bandwidth and storage are constrained. We formalize the problem as a Wardrop equilibrium over a time-discretized graph. Driven by our findings, we propose RECOR, a centralized REsource-Constrained ORacle-based DTN routing mechanism, which spreads the demand across multiple store-carry forward paths to satisfy the node storage and link transport constraints observed in intervention situations. By applying the proposed mechanism to real Bluetooth-based DTN traces, we show that the transmission bottleneck can be compensated, but only up to a certain extent, by increasing storage capacity and delay. We also analyze the benefit of strategies that provide more resources to highly connected nodes (e.g. ambulances and firetrucks) which can then feed incentives and policies for DTN network engineering. Finally, the idea presented here is general and suggests the necessity of trace-driven simulation and specific modeling tools for appropriate design of future DTN resource management policies.

The rise of location-enabled mobile phones and location based services offers a great opportunity to apply personalization and recommender system technology to people's everyday lives. A variety of digital traces can now be used to infer how people move about their city and extract their context and habits. Personalization and recommender systems, potentially merged with the data that people store online (e.g., social networks, web ratings), can then not only be used to recommend new places and events that they may find interesting to attend, but, more broadly, personalize and enhance any service that people find themselves using.

Providers such as YouTube offer easy access to multimedia content to millions, generating high bandwidth and storage demand on the Content Delivery Networks they rely upon. More and more, the diffusion of this content happens on online social networks such as Facebook and Twitter, where social cascades can be observed when users increasingly repost links they have received from others. In this paper we describe how geographic information extracted from social cascades can be exploited to improve caching of multimedia files in a Content Delivery Network. We take advantage of the fact that social cascades can propagate in a geographically limited area to discern whether an item is spreading locally or globally. This informs cache replacement policies, which utilize this information to ensure that content relevant to a cascade is kept close to the users who may be interested in it. We validate our approach by using a novel dataset which combines social interaction data with geographic information: we track social cascades of YouTube links over Twitter and build a proof-of-concept geographic model of a realistic distributed Content Delivery Network. Our performance evaluation shows that we are able to improve cache hits with respect to cache policies without geographic and social information.

The increasing penetration of smart devices with networking capability form novel networks. Such networks, also referred as pocket switched networks (PSNs), are intermittently connected and represent a paradigm shift of forwarding data in an ad hoc manner. The social structure and interaction of users of such devices dictate the performance of routing protocols in PSNs. To that end, social information is an essential metric for designing forwarding algorithms for such types of networks. Previous methods relied on building and updating routing tables to cope with dynamic network conditions. On the downside, it has been shown that such approaches end up being cost ineffective due to the partial capture of the transient network behavior. A more promising approach would be to capture the intrinsic characteristics of such networks and utilize them in the design of routing algorithms. In this paper, we exploit two social and structural metrics, namely centrality and community, using real human mobility traces. The contributions of this paper are two-fold. First, we design and evaluate BUBBLE, a novel social-based forwarding algorithm, that utilizes the aforementioned metrics to enhance delivery performance. Second, we empirically show that BUBBLE can substantially improve forwarding performance compared to a number of previously proposed algorithms including the benchmarking history-based PROPHET algorithm, and social-based forwarding SimBet algorithm.

Pocket Switched Networks take advantage of social contacts to opportunistically create data paths over time. This work employs empirical traces to examine the effect of the human contact process on data delivery in such networks. The contact occurrence distribution is found to be highly uneven: contacts between a few node pairs occur too frequently, leading to inadequate mixing in the network, while the majority of contacts occur rarely, but are essential for global connectivity. This distribution of contacts leads to a significant variation in the fraction of node pairs that can be connected over time windows of similar duration. Good time windows tend to have a large clique of nodes that can all reach each other. It is shown that the clustering coefficient of the contact graph over a time window is a good predictor of achievable connectivity. We then examine all successful paths found by flooding and show that though delivery times vary widely, randomly sampling a small number of paths between each source and destination is sufficient to yield a delivery time distribution close to that of flooding over all paths. This result suggests that the rate at which the network can deliver data is remarkably robust to path failures.

There is substantial interest in the effect of human mobility patterns on opportunistic communications. Inspired by recent work revisiting some of the early evidence for a Lévy flight foraging strategy in animals, we analyse datasets on human contact from real world traces. By analysing the distribution of inter-contact times on different time scales and using different graphical forms, we find not only the highly skewed distributions of waiting times highlighted in previous studies but also clear circadian rhythm. The relative visibility of these two components depends strongly on which graphical form is adopted and the range of time scales. We use a simple model to reconstruct the observed behaviour and discuss the implications of this for forwarding efficiency.

By identifying the shortcomings of using RTT to evaluate the quality of different paths in a multipath scenario, we propose a Relative Delay Estimator (RDE) to compare the relative one way delay of different paths without clock synchronisation. This estimator enables the comparison and selection of the best forward and backward paths, in terms of delay. As an initial application of RDE, we design a novel retransmission policy (NcRDE). The main novelty of this policy is that, from the multiple paths available, the path chosen for retransmission is according to the value of one way delay. We also present an extension to this scheme that takes path failures into account (PF-NcRDE). Simulation results show that, when compared with recently proposed retransmission policies, NcRDE can improve throughput when the different paths have different forward and backward delays. Also, in case of path failure PF-NcRDE enhances the performance significantly over NcRDE.

A city offers thousands of social events a day, and it is difficult for dwellers to make choices. The combination of mobile phones and recommender systems can change the way one deals with such abundance. Mobile phones with positioning technology are now widely available, making it easy for people to broadcast their whereabouts, recommender systems can now identify patterns in people's movements in order to, for example, recommend events. To do so, the system relies on having mobile users who share their attendance at a large number of social events: cold-start users, who have no location history, cannot receive recommendations. We set out to address the mobile cold-start problem by answering the following research question: how can social events be recommended to a cold-start user based only on his home location? To answer this question, we carry out a study of the relationship between preferences for social events and geography, the first of its kind in a large metropolitan area. We sample location estimations of one million mobile phone users in Greater Boston, combine the sample with social events in the same area, and infer the social events attended by 2,519 residents. Upon this data, we test a variety of algorithms for recommending social events. We find that the most effective algorithm recommends events that are popular among residents of an area. The least effective, instead, recommends events that are geographically close to the area. This last result has interesting implications for location-based services that emphasize recommending nearby events.

One of the major concerns of IPTV network deployment is channel switching (or zapping) delay. This delay can add up to two seconds or more, and its main culprits are synchronisation and buffering. By analysing an extensive dataset - comprising 255 thousand users, 150 TV channels, and covering a 6-month period - we have observed that most channel switching events are linear: it is very common the user switching up or down to the next TV channel. This fact led us to the proposal, in this paper, of a simple mechanism to reduce channel switching delay. Our proposal is to send the neighbouring channels (i.e., channels adjacent to the requested one) to the Set Top Box (STB) during zapping periods. If the user switches to any of these channels the switching latency is virtually eliminated, not affecting therefore user's experience. Notwithstanding the simplicity of this scheme, trace-driven simulations show that the zapping delay can be virtually eliminated for a significant percentage of channel switching requests. As an example, by sending the previous and the next channel concurrently with the requested one, for only one minute after a zapping event, switching delay is eliminated for around 45% of all channel switching requests. Furthermore, this simple scheme has a performance close to that of an ideal predictor, while the increase of bandwidth utilisation in the access link is negligible.

The advancements of diverse radio technologies and emerging applications have spawned increasing heterogeneity in mobile ad hoc networks (MANETs). But the collaborative nature of communications and operations often requires that these heterogeneous MANETs to be interoperable. Nonetheless, the existing interconnection protocols designed for the Internet (namely inter-domain routing protocol such as BGP) are not adequate for handling the unique challenges in MANETs. In this paper, we present a novel Inter-MANET Routing protocol called InterMR that can handle the heterogeneity and dynamics of MANETs. Our first contribution is an Inter-MANET address scheme based on a variety of node attributes (e.g., symbolic name, property, etc.); this allows dynamic merging/split of network topologies without a separate Name Server. Our second contribution is to provide a seamless routing mechanism across heterogeneous MANETs without modifying the internal routing mechanisms in each MANET. The proposed scheme can transparently adapt to topological changes due to node mobility in MANETs by dynamically assigning the gateway functionalities. We show, by packet-level simulation, that the performance of InterMR can be improved by up to 112% by adaptive gateway assignment functionalities. We also show that InterMR is scalable with only modest overhead by analysis.

Is the temporal dimension alone sufficient to characterize contacts in opportunistic networks? Several studies analyze the temporal aspect of contacts with significant results concerning contact and inter-contact distributions. Nevertheless, only the temporal dimension does not give a complete overview of contact characterization. In this paper, we propose the surround indicator as a metric to exhibit the contact's surrounding environment in opportunistic networks. We evaluate the surround indicator on two existing datasets and show that contacts have too heterogeneous and too unstable surrounds to be considered only in terms of duration. Besides a large variability of the surrounding environment within the duration of a single contact, it is frequent to observe contacts of identical duration that exhibit differences in their surrounds of more than a hundred times.

An increasing number of sensor networks have been deployed to monitor a variety of conditions and situations. At the same time, more and more applications are starting to rely on the data from sensor networks to provide users with (near) real-time information and conditions. This increasing demand of users for accurate information about natural and surrounding phoenomena is creating a business case for application providers.

Complex systems researchers have looked to the Internet as a possible source of interesting emergent behaviour. Indeed, some high-profile failures, and some low-level phenomena, might easily be construed as evidence of a complex system. In this paper, I look at the local and global consequences of the Internet design, and show that few, if any, of these problems are actually consequences of emergent properties in the pure technical sense. However, there are lessons for network architecture from these problems. The influence of local decisions on global behaviour of the network is a source of some of the difficulties that protocol designers must cope with, but it is also a source of great wealth and innovation, and as such should be regarded in a positive light.

Technology is taking us to a world where myriads of heavily networked devices interact with the physical world in multiple ways, and at multiple scales, from the global Internet scale down to micro- and nano-devices. Many of these devices are highly mobile and autonomous, and must adapt to the surrounding environment in a totally distributed and unsupervised way. fundamental research challenge is the design of robust decentralized computing systems, which are capable of operating under changing environments, and yet exhibit the desired behavior and response time, under unpredictable operating constraints, such as traffic demand, energy consumption, size, and processing power. These systems should be able to adapt and learn how to react to unforeseen scenarios, as well as to display desired emergent properties.

We envision new communication paradigms, using physical dynamic interconnectedness among people. Delay Tolerant Networks (DTNs) are a new communication paradigm to support such network environments, and our focus is a type of DTN that provides intermittent communication for humans carrying mobile devices: the Pocket Switched Network (PSN). Information propagation in PSNs is highly influenced by human connectivity networks, i.e. social networks. In our previous work, we have exploited constructing weighted networks using characteristics of pair connections such as the duration of contact time and frequency of contacts from time series of human connectivity network traces. The approach we took is based on empirical and heuristic and the focus is finding a single aggregated logical network structure. The physical network topology in the real world is time dependent and it is a complex task to describe its dynamics. This paper aims to identify dynamics of meeting groups in human connectivity traces, where meeting groups are expected to be a group interacting among the nodes in physical space. Thus, we define dasiameeting grouppsila differently from dasiacommunitypsila. We exploit statistical approach that provides quantitative attributes to uncover meeting groups. We identify the power law behavior of meetings that is important for supporting to understanding dynamics of information flow between meeting groups and building group oriented communication protocol.

The recently proposed packet switched network paradigm takes advantage of human social contacts to opportunistically create data paths over time. Our goal is to examine the effect of the human contact process on data delivery. We find that the contact occurrence distribution is highly uneven: contacts between a few node-pairs occur too frequently, leading to inadequate mixing in the network, while the majority of contacts are rare, and essential for connectivity. This distribution of contacts leads to a significant variation in performance over short time windows. We discover that the formation of a large clique core during the window is correlated with the fraction of data delivered, as well as the speed of delivery. We then show that the clustering co-efficient of the contact graph over a time window is a good predictor of performance during the window. Taken together, our findings suggest new directions for designing forwarding algorithms in ad-hoc or delay-tolerant networking schemes using humans as data mules.

Peer-to-peer distributed virtual environments (DVE's) distribute state tracking and state transitions. Many DVE's- such as online games- require ways to fairly determine the outcome of probabilistic events. While trivial when a trusted third party is involved, resolving these actions fairly between adversaries without a trusted third party is much more difficult. This paper proposes the Pairwise Random Protocol (PRP), which uses secure coin flipping to enable adversaries to fairly determine the result of a probabilistic event without a trusted third party. Three different variations of PRP are presented, and the time impact and network overhead are examined. We conclude that PRP enables DVE’s to distribute the work of determining probabilistic events between adversaries without loss of security or fairness, and with acceptable overhead.

As researchers we have a duty to communicate our ideas. As communications researchers, we are privileged to study the technology and phenomena that are so transforming human society. We seek to understand and enhance the very tools with which we can convey our contributions to human knowledge.

A grid-powered Web Geographical Information Science (GIS)/Web Processing Service (WPS) system has been developed for archiving and distributing large volumes of geospatial data. However, users, WPS servers, and data resources are always distributed across different locations, attempting to access and archive geospatial data from a GIS survey via conventional Hypertext Transport Protocol, Network File System Protocol, and File Transfer Protocol, which often encounters long waits and frustration in wide area network (WAN) environments. To provide a “local-like” performance, a WAN/grid-optimized protocol known as “GridJet” developed at our lab was used as the underlying engine between WPS servers and clients, which utilizes a wide range of technologies including the one of paralleling the remote file access. No change in the way of using software is required since the multistreamed GridJet protocol remains fully compatible with the existing IP infrastructures. Our recent progress includes a real-world test that PyWPS and Google Earth over the GridJet protocol beat those over the classic ones by a factor of two to eight, where the distribution/archiving distance is over 10 000 km.

In the above-named work the name of one of the authors is incorrectly given. Included here also is the biography with the missing IEEE membership information.

For half a century, television has been a dominant and pervasive mass media, driving many technological advances. Despite its widespread usage and importance to emerging applications, the ingrained TV viewing habits are not completely understood. This was primarily due to the difficulty of instrumenting monitoring devices at individual homes at a large scale. The recent boom of Internet TV (IPTV) has enabled us to monitor the user behavior and network usage of an entire network. Such analysis can provide a clearer picture of how people watch TV and how the underlying networks and systems can better adapt to future challenges. In this paper, we present the first analysis of IPTV workloads based on network traces from one of the world's largest IPTV systems. Our dataset captures the channel change activities of 250,000 households over a six month period. We characterize the properties of viewing sessions, channel popularity dynamics, geographical locality, and channel switching behaviors. We discuss implications of our findings on networks and systems, including the support needed for fast channel changes. Our data analysis of an operational IPTV system has important implications on not only existing and future IPTV systems, but also the design of the open Internet TV distribution systems such as Joost and BBC's iPlayer that distribute television on the wider Internet.

In this paper we seek to improve our understanding of human mobility in terms of social structures, and to use these structures in the design of forwarding algorithms for Pocket Switched Networks (PSNs). Taking human mobility traces from the real world, we discover that human interaction is heterogeneous both in terms of hubs (popular individuals) and groups or communities. We propose a social based forwarding algorithm, BUBBLE, which is shown empirically to improve the forwarding efficiency significantly compared to oblivious forwarding schemes and to PROPHET algorithm. We also show how this algorithm can be implemented in a distributed way, which demonstrates that it is applicable in the decentralised environment of PSNs.

Poisoning attacks in the Peer-to-Peer (P2P) content sharing service have become a serious security problem on the global Internet due to the features of P2P systems such as self-organization, self-maintenance, etc. In this paper, we propose a novel poisoning-resistant security framework based on the notion that the content providers would be the only trusted sources to verify the integrity of the requested content. To provide the mechanisms of availability and scalability, a content provider publishes the information of his shared contents to a group of content maintainers self-organized in a security overlay, so that a content requestor can verify the integrity of the requested content from the associated content maintainers. Two defense functions are first carried out - filtering out malicious activities and selecting the authentic content version. Then, the content requestor can perform the content integrity verification while downloading and take prompt protection actions to handle content poisoning attacks. To further enhance the system performance, we devise a scalable probabilistic verification scheme. The evaluation results illustrate that our framework can effectively and efficiently defend against content poisoning in various scenarios.

Incentive mechanisms are essential components of peer-to-peer systems for file sharing such as BitTorrent, since they enforce peers to share their resources and to participate. Recent P2P systems that distribute live multimedia streams take their inspiration from BitTorrent, but have not defined incentive mechanisms appropriate to the nature of continuous media. In this article, we uncover the way that the incentive mechanisms in BitTorrent are not well suited to streaming live multimedia, and based on P2P systems that we have measured, we propose a new incentive mechanism designed for distribution of live multimedia streaming over a P2P network.

Developing protocols and applications for opportunistic networking can represent a daunting task given the many aspects that must be taken into consideration, such as intermittent connectivity, smart choice among multiple interfaces and intelligent data storage. The implementation of these protocols can be based on generic layer-less communication frameworks that provide programming abstractions for the extraction and analysis of social, colocation and mobility information and allows data exchange by means of heterogeneous devices. We propose Gently, a novel fully implemented solution which combines techniques of context awareness and social knowledge to concretely solve issues related to opportunistic forwarding. More precisely, Gently is born as the combination of the Context-aware Adaptive Routing (CAR) and the socially aware LABEL protocol. We discuss the implementation of our solution on top of the layer-less Haggle framework presenting the key design choices and the lessons learnt.

Worm containment must be automatic because worms can spread too fast for humans to respond. Recent work proposed network-level techniques to automate worm containment; these techniques have limitations because there is no information about the vulnerabilities exploited by worms at the network level. We propose Vigilante, a new end-to-end architecture to contain worms automatically that addresses these limitations. In Vigilante, hosts detect worms by instrumenting vulnerable programs to analyze infection attempts. We introduce dynamic data-flow analysis : a broad-coverage host-based algorithm that can detect unknown worms by tracking the flow of data from network messages and disallowing unsafe uses of this data. We also show how to integrate other host-based detection mechanisms into the Vigilante architecture. Upon detection, hosts generate self-certifying alerts (SCAs), a new type of security alert that can be inexpensively verified by any vulnerable host. Using SCAs, hosts can cooperate to contain an outbreak, without having to trust each other. Vigilante broadcasts SCAs over an overlay network that propagates alerts rapidly and resiliently. Hosts receiving an SCA protect themselves by generating filters with vulnerability condition slicing : an algorithm that performs dynamic analysis of the vulnerable program to identify control-flow conditions that lead to successful attacks. These filters block the worm attack and all its polymorphic mutations that follow the execution path identified by the SCA. Our results show that Vigilante can contain fast-spreading worms that exploit unknown vulnerabilities, and that Vigilante's filters introduce a negligible performance overhead. Vigilante does not require any changes to hardware, compilers, operating systems, or the source code of vulnerable programs; therefore, it can be used to protect current software binaries.

The recent work on COPE by Katti et al. demonstrates a practical application of network coding to wireless multihop networks. We note, however, that the opportunistic nature of COPE leaves it at the mercy of higher and lower layer protocols to create coding opportunities spontaneously. In this paper, we go one step beyond COPE's opportunism and study how to create coding opportunities in a more deterministic, yet still practical way. We start from the insight that in two-way traffic the existence of coding opportunities can be guaranteed through carefully co-ordinated packet scheduling, and establish general properties of protocols that are able to achieve this. We then propose Near-Optimal Coordinated Coding (noCoCo), a cross-layer scheme that integrates per-hop packet scheduling, network coding, and congestion control in a novel way. Extensive simulations show that noCoCo significantly outperforms standard non-coding approaches as well as COPE in terms of network throughput, delay and transmission overhead.

Network coding is seen as a promising technique to improve network throughput. In this paper, we study two important problems in localized network coding in wireless networks, which only requires each node to know about and coordinate with one-hop neighbors. In particular, we first establish a condition that is both necessary and sufficient for useful coding to be possible. We show this condition is much weaker than expected, and hence allows a variety of coding schemes to suit different network conditions and application preferences. Based on the understanding we establish, we are able to design a robust coding technique called loop coding that can improve network throughput and TCP throughput simultaneously.

The emergence of Delay Tolerant Networks (DTNs) has culminated in a new generation of wireless networking. We focus on a type of human-to-human communication in DTNs, where human behaviour exhibits the characteristics of networks by forming a community. We show the characteristics of such networks from extensive study of real-world human connectivity traces. We exploit distributed community detection from the trace and propose a Socio-Aware Overlay over detected communities for publish/subscribe communication. Centrality nodes have the best visibility to the other nodes in the network. We create an overlay with such centrality nodes from communities. Distributed community detection operates when nodes (i.e. devices) are in contact by gossipping, and subscription propagation is performed along with this operation. We validate our message dissemination algorithms for publish/subscribe with connectivity traces.

We study data transfer opportunities between wireless devices carried by humans. We observe that the distribution of the intercontact time (the time gap separating two contacts between the same pair of devices) may be well approximated by a power law over the range [10 minutes; 1 day]. This observation is confirmed using eight distinct experimental data sets. It is at odds with the exponential decay implied by the most commonly used mobility models. In this paper, we study how this newly uncovered characteristic of human mobility impacts one class of forwarding algorithms previously proposed. We use a simplified model based on the renewal theory to study how the parameters of the distribution impact the performance in terms of the delivery delay of these algorithms. We make recommendations for the design of well-founded opportunistic forwarding algorithms in the context of human-carried devices

There is a critical need for new thinking regarding overload traffic management in sensor networks. It has now become clear that experimental sensor networks (e.g., mote networks) and their applications commonly experience periods of persistent congestion and high packet loss, and in some cases even congestion collapse. This significantly impacts application fidelity measured at the physical sinks, even under light to moderate traffic loads, and is a direct product of the funneling effect ; that is, the many-to-one multihop traffic pattern that characterizes sensor network communications. Existing congestion control schemes are effective at mitigating congestion through rate control and packet drop mechanisms, but do so at the cost of significantly reducing application fidelity measured at the sinks. To address this problem we propose to exploit the availability of a small number of all wireless, multiradio virtual sinks that can be randomly distributed or selectively placed across the sensor field. Virtual sinks are capable of siphoning off data events from regions of the sensor field that are beginning to show signs of high traffic load. In this paper, we present the design, implementation, and evaluation of Siphon , a set of fully distributed algorithms that support virtual sink discovery and selection, congestion detection, and traffic redirection in sensor networks. Siphon is based on a Stargate implementation of virtual sinks that uses a separate longer range radio network (based on IEEE 802.11) to siphon events to one or more physical sinks, and a short-range mote radio to interact with the sensor field at siphon points. Results from analysis, simulation and an experimental 48 Mica2 mote testbed show that virtual sinks can scale mote networks by effectively managing growing traffic demands while minimizing any negative impact on application fidelity. Additionally, we show the scheme is competitive with respect to energy consumption compared to a network composed of only motes.

In network coding, a router in the network mixes information from different flows. In the seminal work by Ahlswede et al [1], network coding is established as a technique to potentially increase the network capacity.

It is widely believed that identifying communities in an ad hoc mobile communications system, such as a pocket switched network, can reduce the amount of traffic created when forwarding messages, but there has not been any empirical evidence available to support this assumption to date. In this paper, we show through use of real experimental human mobility data, how using a small label, identifying users according to their affiliation, can bring a large improvement in forwarding performance, in term of both delivery ratio and cost.

String comparison algorithms, inspired by methods used in bioinformatics, have recently gained popularity in network applications. In this paper we demonstrate the need for careful selection of alignment models if such algorithms are to yield the desired results when applied to network traffic. We introduce a novel variant of the Jacobson-Vo algorithm employing a flexible gap-minimising alignment model suitable for network traffic, and find that our software implementation outperforms the commonly used Smith-Waterman approach by a factor of 33 on average and up to 58.5 in the best case on a wide range of network protocols.

Abstract — Studying transfer opportunities between wireless devices carried by humans, we observe that the distribution of the inter-contact time, that is the time gap separating two contacts of the same pair of devices, exhibits a heavy tail such as one of a power law, over a large range of value. This observation is confirmed on six distinct experimental data sets. It is at odds with the exponential decay implied by most mobility models. In this paper, we study how this new characteristic of human mobility impacts a class of previously proposed forwarding algorithms. We use a simplified model based on the renewal theory to study how the parameters of the distribution impact the delay performance of these algorithms. We make recommendation for the design of well founded opportunistic forwarding algorithms, in the context of human carried devices. I.

E-business organizations commonly trade services together with quality of service (QoS) guarantees that are often dynamically agreed upon prior to service provisioning. Violating agreed QoS levels incurs penalties and hence service providers agree to QoS requests only after assessing the resource availability. Thus the system should, in addition to providing the services: (i) monitor resource availability, (ii) assess the affordability of a requested QoS level, and (iii) adapt autonomically to QoS perturbations which might undermine any assumptions made during assessment. This paper will focus on building such a system for reliably multicasting messages of arbitrary size over a loss-prone network of arbitrary topology such as the Internet. The QoS metrics of interest will be reliability, latency and relative latency. We meet the objectives (i)-(iii) by describing a network monitoring scheme, developing two multicast protocols, and by analytically estimating the achievable latencies and reliability in terms of controllable protocol parameters. Protocol development involves extending in two distinct ways an existing QoS-adaptive protocol designed for a single packet. Analytical estimation makes use of experimentally justified approximations and their impact is evaluated through simulations. As the protocol extension approaches are complementary in nature, so are the application contexts they are found best suited to; e.g., one is suited to small messages while the other to large messages

This paper considers the steady-state solution of Continuous Time Markov Chains (CTMCs). CTMCs are a widely used formalism for the performance analysis of computer and communication systems. A large variety of useful performance measures can be derived from a CTMC via the computation of its steady-state probabilities. However, CTMC models for realistic systems are very large. We address this largeness problem in this paper by considering parallelisation of implicit methods. In particular, we consider a modified form of Multi- Terminal Binary Decision Diagrams (MTBDDs) to compactly store CTMCs, and, using Jacobi iterative method, we present a parallel method for the CTMC steady-state solution. Employing a 24-node processor bank, we analyse our parallel implicit method using the experimental results for three widely used CTMC benchmark models, with well over a billion states and sixteen billion transitions.

With today's penetration in volume and variety of information flowing across the Internet, data and services are experiencing various issues with the TCP/IP infrastructure, most notably availability, reliability and mobility. Therefore, a critical infrastructure is highly desireable, in particular for multimedia streaming applications. So far the proposed approaches have focused on applying application-layer routing and path monitoring for reliability and on enforcing stateful packet filters in hosts or network to protect against Denial of Service (DoS) attacks. Each of them solves its own aspect of the problem, trading scalability for availability and reliability among a relatively small set of nodes, yet there is no single overall solution available which addresses these issues in a large scale.We propose an alternative overlay network architecture by introducing a set of generic functions in network edges and end hosts. We conjecture that the network edge constitutes a major source of DoS, resilience and mobility issues to the network, and propose a new solution to this problem, namely the General Internet Signaling Transport (GIST) Overlay Networking Extension, or GONE. The basic idea of GONE is to create a half-permanent overlay mesh consisting of GONE-enabled edge routers, which employs capability-based DoS prevention and forwards end-to-end user traffic using the GIST messaging associations. GONE's use of GIST on top of SCTP allows multi-homing, multi-streaming and partial reliability, while only a limited overhead for maintaining the messaging association is introduced. In addition, upon the services provided by GONE overlays, hosts are identified by their unique host identities independent of their topologies location, and simply require (de-)multiplexing instead of the traditional connection management and other complex functionality in the transport layer. As a result, this approach offers a number of advantages for upper layer end-to-end applications, including intrinsic provisioning of resilience and DoS prevention in a dynamic and nomadic environment.

This paper proposes COPE, a new architecture for wireless mesh networks. In addition to forwarding packets, routers mix (i.e., code) packets from different sources to increase the information content of each transmission. We show that intelligently mixing packets increases network throughput. Our design is rooted in the theory of network coding. Prior work on network coding is mainly theoretical and focuses on multicast traffic. This paper aims to bridge theory with practice; it addresses the common case of unicast traffic, dynamic and potentially bursty flows, and practical issues facing the integration of network coding in the current network stack. We evaluate our design on a 20-node wireless network, and discuss the results of the first testbed deployment of wireless network coding. The results show that COPE largely increases network throughput. The gains vary from a few percent to several folds depending on the traffic pattern, congestion level, and transport protocol.

Abstract A major challenge in building ‘all‐IP’ wireless access networks, besides the use of IP as the unifying layer, relates to the transparency of the IP handoff process as the mobile node (MN) transits across heterogeneous wireless network domains in IPv6 mobility management. Transparency in IP handoffs, however, must be effected in two separate contexts: IP‐addressing and (re‐)connection latency. Excessive delays during an IP handoff degrades the seamlessness of IP transmission between the MN and its peers. Motivated by experimental results over heterogeneous wireless networks, we discuss why dynamic establishment of IP context‐state can help address these limitations that seem inherent in heterogeneous environments. To this end, we provide an in‐depth evaluation of Proactive Mobile IPv6 by means of simulations. Our study contrasts the efficiency of proactive context‐state establishment, between candidate points of attachment (PoAs), against reactive MIPv6 standard practices over handoff delay, jitter and associated packet loss. Departing from reactive MIPv6 standards, this study brings new insights towards seamless handoffs by investigating the influential aspects of non‐determinism in the mobility pattern of the MN. In addition, it demonstrates quantitatively the performance benefit attained by maximising MN's service utility through PoA selectivity embedded in MN IPv6 handoff decision. Copyright © 2006 John Wiley & Sons, Ltd.

In recent years, much work has been done on attempting to scale multicast data transmission to hundreds or thousands of receivers. There are, however, many situations where an application might involve transmission to just ten or twenty sites. The European Space Agency, for example, carry out regular multi-gigabyte bulk data transfers to a handful of destinations.Using multicast for this type of application can provide significant benefits including reduced load on the transmitter, an overall reduction in network traffic, and consequently shorter data transfer times.In this paper we take a fresh look at the problem of deploying reliable multicast. So far, there has been no convincing solution to achieve this. We present a simple hybrid solution which has not been proposed before. The approach taken is to combine unicast with multicast by modifying TCP to support multicast transfers, and run this modified TCP engine over UDP as a userspace transport protocol.Our goal is clear: reliable bulk data delivery to a moderate number of sites. Unlike some other multicast protocols, our work is complete: we have designed, implemented, deployed and evaluated a protocol which meets this goal.

The high bandwidth and low latency of the modern internet has made possible the deployment of distributed computing platforms. The XenoServe platform provides a distributed computing platform open to all and presents three major new challenges for resource discovery: Firstly, network location is key for effectively provisioning services, to mitigate against high-latency, high-load or component failure. Secondly, many services require a presence on several servers, with inter-related requirements. Finally, as the platform is open with respect to users and servers, large numbers of queries and updates are expected.To address these requirements we introduce and evaluate XenoSearch, a new distributed service for selecting the machines to host components of multi-node distributed systems and which is uniquely able to express and efficiently answer complex queries with inter-related location constraints. We demonstrate that XenoSearch represents a trade-off between accuracy and query time which avoids exhaustive search and supports multiple resources. In addition the performance of the algorithm and the quality of its server selections is investigated and the performance of the distributed service shown to be invariant as the number of nodes or items indexed increases.

In recent years, much work has been done on attempting to scale multicast data transmission to hundreds or thousands of receivers. There are, however, many situations where an application might involve transmission to just ten or twenty sites. Using multicast for this type of application can provide significant benefits including reduced load on the transmitter an overall reduction in network traffic, and consequently shorter data transfer times. In this project, we are investigating how partial or incomplete multicast can be exploited alongside reliable unicast to improve both speed and efficiency of data transfers while maintaining reliability. The approach taken is to combine unicast with multicast by modifying TCP to support multicast transfers, and run this modified TCP engine over UDP as a userspace transport protocol. We describe the work to date on the design and implementation, and provide experimental results from our tests across both local and wide area networks.

In this paper we focus on the problems of maintaining ad hoc network connectivity in the presence of node mobility whilst providing globally efficient and robust routing. The common approach among existing wireless ad hoc routing solutions is to establish a global optimal path between a source and a destination. We argue that establishing a globally optimal path is both unreliable and unsustainable as the network diameter, traffic volume and number of nodes all increase in the presence of moderate node mobility. To address this we propose landmark guided forwarding (LGF), a protocol that provides a hybrid solution of topological and geographical routing algorithms. We demonstrate that LGF is adaptive to unstable connectivity and scalable to large networks. Our results indicate therefore that landmark guided forwarding converges much faster, scales better and adapts well within a dynamic wireless ad hoc environment in comparison to existing solutions.

We propose a novel and plausibly realistic application scenario for mobile ad hoc networks in the form of a radio dispatch system. We evaluate the system from both financial and technical perspectives to gain a complete picture of its feasibility. Using a realistic mobility and propagation model drawn from real world data we investigate the effects of node density, connection times and traffic congestion on the network coverage. We discuss design considerations in the light of the results. These findings are not limited to this particular scenario but are applicable to any mobile ad hoc system operating in similar conditions.

There is a critical need for new thinking regarding overload traffic management in sensor networks. It has now become clear that experimental sensor networks (e.g., mote networks) and their applications commonly experience periods of persistent congestion and high packet loss, and in some cases even congestion collapse. This significantly impacts application fidelity measured at the physical sinks, even under light to moderate traffic loads, and is a direct product of the funneling effect; that is, the many-to-one multi-hop traffic pattern that characterizes sensor network communications. Existing congestion control schemes are effective at mitigating congestion through rate control and packet drop mechanisms, but do so at the cost of significantly reducing application fidelity measured at the sinks. To address this problem we propose to exploit the availability of a small number of all wireless, multi-radio virtual sinks that can be randomly distributed or selectively placed across the sensor field. Virtual sinks are capable of siphoning off data events from regions of the sensor field that are beginning to show signs of high traffic load. In this paper, we present the design, implementation, and evaluation of Siphon, a set of fully distributed algorithms that support virtual sink discovery and selection, congestion detection, and traffic redirection in sensor networks. Siphon is based on a Stargate implementation of virtual sinks that uses a separate longer-range radio network (based on IEEE 802.11) to siphon events to one or more physical sinks, and a short-range mote radio to interact with the sensor field at siphon points. Results from analysis, simulation and an experimental 48 Mica2 mote testbed show that virtual sinks can scale mote networks by effectively managing growing traffic demands while minimizing the impact on application fidelity.

Worm containment must be automatic because worms can spread too fast for humans to respond. Recent work has proposed network-level techniques to automate worm containment; these techniques have limitations because there is no information about the vulnerabilities exploited by worms at the network level. We propose Vigilante, a new end-to-end approach to contain worms automatically that addresses these limitations. Vigilante relies on collaborative worm detection at end hosts, but does not require hosts to trust each other. Hosts run instrumented software to detect worms and broadcast self-certifying alerts (SCAs) upon worm detection. SCAs are proofs of vulnerability that can be inexpensively verified by any vulnerable host. When hosts receive an SCA, they generate filters that block infection by analysing the SCA-guided execution of the vulnerable software. We show that Vigilante can automatically contain fast-spreading worms that exploit unknown vulnerabilities without blocking innocuous traffic.

Article Share on Peerlessness is next to godlessness Author: Jon Crowcroft View Profile Authors Info & Claims SOSP '05: Proceedings of the twentieth ACM symposium on Operating systems principlesOctober 2005 Pages 1–5https://doi.org/10.1145/1095810.1118574Online:23 October 2005Publication History 0citation149DownloadsMetricsTotal Citations0Total Downloads149Last 12 Months1Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

In this work, we apply network coding to unicast in wireless mesh networks to improve network throughput while reducing the bandwidth and power requirements. Our Opportunistic Coding protocol encodes and decodes packets based on distributed and local decisions, in order to reduce the number of transmissions required in forwarding. Unlike prior work which focuses on theorectical analysis, we take a systems approach and study the performance through both emulations and testbed experiments. Our protocol aims to balance the tradeoff between reliable and efficient packet delivery. Preliminary results show that the real benefit of network coding can exceed the theorectically predicted gain, due to cross-layer interactions.

All over the world Global System for Mobile Communication (GSM) cellular mobile networks have been upgraded to support the "always-on" general packet radio service (GPRS). Despite the apparent availability of levels of bandwidth not dissimilar to that provided by conventional fixed-wire telephone modems, the user experience using GPRS is still considerably poor. In this paper, we examine the performance of protocols such as transmission control protocol (TCP) over GPRS, and show how certain network characteristics interact badly with TCP to yield problems such as: link underutilization for short-lived flows, excess queueing for long-lived flows, acknowledgment bunching, poor loss recovery, and gross unfairness between competing flows. We present the design and implementation of a transparent TCP proxy that mitigates many of these problems without requiring any changes to the TCP implementations in either mobile or fixed-wire end systems. The proxy is interposed in the cellular provider's network, and splits TCP connections transparently into two halves-the wired and wireless sides. Connections destined for the same mobile host are treated as an aggregate due to their statistical dependence. We demonstrate packet scheduling and flow control algorithms that use information shared between the connections to maximize performance of the wireless link, while interworking with unmodified TCP peers. We also demonstrate how fairness between flows and response to loss is improved, and that queueing and, hence, network latency is reduced. We discuss how TCP enhancing proxies could be transparently deployed, and conclude that installing such a proxy into GPRS network would be of significant benefit to users.

The vision of Ubiquitous Computing [22] creates the world in which information is omnipresent, migrating seamlessly through the environment to be accessible whenever and wherever needed. Such a vision poses substantial challenges to information security and privacy protection.Unlike in traditional, static, execution environments, information in the Ubiquitous world is exposed, throughout its lifetime, to constantly varying security and privacy threats caused by the inherent dynamicity and unpredictability of the new computing environment and its mobility. Existing data protection mechanisms, built for non- or predictably slowly-changing environments, are unable to strike the balance in the information availability vs. security and privacy threat trade-off in the Ubiquitous world thus hindering the feasibility of the overall vision.In this paper, we present our initial work on a novel paradigm for information security and privacy protection in the ubiquitous world. We model security and privacy threats through sets of contextual attributes and mitigate the projected risks through proactive and reactive data format transformations, subsetting and forced migrations while trying to maximize information availability. We also try to make the approach flexible, scalable and infrastructure independent, as required by the very vision of the Ubiquitous Computing.

The "location-aware" construction of overlay networks requires the identification of nodes that are efficient with respect to network delay and available bandwidth. In this short paper, we propose Highways to create clusters of nodes using a novel "location-aware" method, based on a scalable and distributed network coordinate system. This helps to build overlay routing tables to achieve better proximity accuracy, thus, providing a mechanism to boost performance in application overlay routing.

Interworking heterogeneous wireless access technologies is an important step towards building the next generation, all-IP wireless access infrastructure. We present an experimental study of inter-network mobility between GPRS Cellular and 802.11b-based WLAN hot-spots, and deeply analyze its impact on active transport TCP flows. Our experiments were conducted over a loosely-coupled, Mobile IPv6-based, GPRS-WLAN experimental testbed. Detailed analysis from packet traces of inter-network (vertical) handovers reveals a number of performance bottlenecks. In particular, the disparity in the round trip time and bandwidth offered by GPRS and WLAN networks, and presence of deep buffers in GPRS, can aggravate performance during vertical handovers. This paper, therefore, summarizes practical experiences and challenges of providing transparent mobility in heterogeneous environments. Based on the observations, we propose a number of network-layer handover optimization techniques, e.g. fast router advertisements (RA), RA caching, binding update (BU) simulcasting and layer-3 based soft handovers that improve performance during vertical handovers. The paper concludes with our experiences of migrating TCP connections, thereby also improving application e.g. FTP, Web performance in this environment.

No abstract available.

The Internet, as an accessible network with global connectivity, presents itself as the likely solution for the supporting infrastructure of cyberspace. In contrast, the field of virtual environments (VE) is fragmented with a wide proliferation of different systems, as a result of the current development trend. However, these divergent systems present a significant overlap of functionality that represents a waste of development resources and a detriment to innovation. This paper proposes Mayhem as a new approach to the design and development of VE systems. Our approach relies heavily on component design methodology coupled with a simple layered approach to system architectures. The combination of the two promotes the construction of systems from smaller building blocks, which may have been developed by different sources, each specializing in the supported functionality. Although Mayhem promotes the trend of middleware, it requires a common base infrastructure - the Java Adaptive Dynamic Environment (JADE). To demonstrate how to build a VE system using Mayhem, the paper describes a simple prototype of a VE and some of its key building blocks.

With the evolution of the QoS-capable third generation wireless networks, the wireless community has been increasingly looking for a framework that can provide an effective, network independent, end-to-end QoS control. In this paper, we first construct such a framework and then describe how dynamic SLA-based control can be used to achieve end-to-end QoS in a wired and wireless (UMTS) environment. The proposed framework, which is an extension of the IST CADENUS project, offers an effective wired-wireless QoS translation, an efficient QoS control and management, and a dynamic SLA policy-based QoS provisioning.

Throughout the world, GSM cellular mobile networks are being upgraded to support the "always-on" General Packet Radio Service (GPRS). Despite the apparent availability of levels of bandwidth not dissimilar to that provided by conventional fixed-wire telephone modems, the user experience using GPRS is currently considerably worse. In this paper we examine the performance of TCP and HTTP over GPRS, and show how certain network characteristics interact badly with TCP to yield problems such as: link under-utilization for short-lived flows, excess queueing for long-lived flows, ACK compression, poor loss recovery, and gross unfairness between competing flows. We present the design and implementation of a transparent TCP proxy that mitigates many of these problems without requiring any changes to the TCP implementations in either mobile or fixed-wire end systems. The proxy transparently splits TCP connections into two halves, the wired and wireless sides. Connections destined for the same mobile host are treated as an aggregate due to their statistical dependence. We demonstrate packet scheduling and flow control algorithms that use information shared between the connections to maximise performance of the wireless link while inter-working with unmodified TCP peers. We also demonstrate how fairness between flows and response to loss is improved, and that queueing and hence network latency is reduced. We conclude that installing such a proxy into GPRS network would be of significant benefit to users.

In the near future, billing for network services will not only be concerned with time or volume based accounting but also in ways of measuring the quality of the service provided. Dynamic price schemes, such as congestion-based charging, have been proposed. In some of these models, the charging infrastructure relies on the distribution of electronic tariffs to end-users machines. The tariff structure includes the price information and an algorithm to calculate the charge. Thus, the monitoring of network usage according to this tariff is essential within these frameworks. However, little attention has been given to the security issues associated with Internet metering in these schemes. This has had a great impact on the new models proposed today, since security has become a major concern in open networks. Systems that naturally have incentive to fraud, such as metering systems used for billing purposes, must deal with security threats in large scale environments. The article compiles the security issues of a dynamic networked system where electronic tariffs and service level agreement (SLA) structures are distributed among service providers and customers. To address these issues, a set of security protocols is outlined.

Large-scale applications are characterized by a large number of dynamic and often interactive group members. The nature of these applications is such that participants are not interested in all the content transmitted. We examine three currently available techniques to scope delivery of content to interested receivers in IP multicast: filtering, where data is filtered by middleware before being passed to the application; addressing, where data is routed only to those receivers that express their interest; and hybrid approaches. We propose a framework that models large-scale application behavior. We use this framework to evaluate the performance of these applications and related protocols when the network is capable of filtering or addressing. Our results show that the current Internet architecture does not efficiently support large-scale applications because it can not efficiently manage multiple multicast groups. We show that network-level addressing is preferred to filtering and hybrid approaches given that groups are easy to create and manage. We highlight areas of research in the multicast architecture to bring about this change.

We discuss the use of interleaving as a bandwidth efficient means of protecting audio streams from the effects of packet loss on the Internet. The adverse effects of interleaving on IP/UDP/RTP header compression are noted and a number of schemes which remedy this problem are discussed.

A chargeable session on the Internet may consist of more than one underlying chargeable service. Typically there will be two, one at the network layer and one at the session layer. Since different applications can have different demands from the network, a generic charging scheme has to separate the service provided by the network from the service provided by an application/service provider. In this paper we propose a pricing model which is session-based and we look at the impact of this on real-time multimedia conferencing over the Internet. In this model, we are trying to allow for the optional integration of charging at the network layer with charging at the session layer, while keeping the underlying technologies still cleanly apart. This paper also highlights the fact that the main problem of pricing application on the Internet is not just a simple case of analyzing the most technically feasible pricing mechanism but also making the solution acceptable to users. We take the position that session-based pricing is easier for end users to accept and understand and show why this is the case in this paper.

The majority of existing systems supporting Large Scale Virtual Environments (LSVE) are based on monolithic architectures, making maintenance, reusability and extensibility difficult at best. An overview of the Java Adaptive Dynamic Environment (JADE) is presented as an alternative to the traditional approach for developing a core infrastructure for VE systems. JADE consists of a light-weight cross-platform kernel with inherent capabilities for dynamic extensibility in run-time. Although the design principles are applicable to other areas, the implementation is targeted at LSVE. The JADE implementation is 100% Java, taking full advantage of the following properties: wide deployment across major operating systems and browsers; comprehensive set of packages; networking capabilities and security mechanisms.

Multicast service is an important part of any modern routing architecture. Motivations for many-to-many multicast include general unpredictability of membership in many real applications; simplicity of the rendezvous for the application programmer; and low cost to end systems and switches in terms of state to maintain for the delivery tree. Shared trees have an even greater advantage over source based trees in the latter respect. Shared trees (as in the CBT model for Internet) are supported in the form of a single logical VC per multicast group (i.e., multipoint-to-multipoint VC or mp-mp VC) in the ATM networks. CRAM, SEAM and SPAM have been previously proposed for supporting mp-mp VC. The work in this paper does a performance comparison of these three schemes, with respect to buffer requirements, end-to-end delay, packet jitter and traffic overhead. Our evaluation is carried out through extensive simulations with different topologies and sender traffic types.

We present a novel congestion control algorithm suitable for use with cumulative, layered data streams in the MBone. Our algorithm behaves similarly to TCP congestion control algorithms, and shares bandwidth fairly with other instances of the protocol and with TCP flows. It is entirely receiver driven and requires no per-receiver status at the sender, in order to scale to large numbers of receivers. It relies on standard functionalities of multicast routers, and is suitable for continuous stream and reliable bulk data transfer. In the paper we illustrate the algorithm, characterize its response to losses both analytically and by simulations, and analyse its behaviour using simulations and experiments in real networks. We also show how error recovery can be dealt with independently from congestion control by using FEC techniques, so as to provide reliable bulk data transfer.

Multi-party collaborative multimedia applications require data to be transmitted reliably and efficiently in order to provide a guaranteed quality of service (QoS). The multimedia applications can vary from distributed games and shared whiteboards to interactive video conferencing. These applications often involve a large number of participants and are interactive in nature, with participants dynamically joining and leaving the applications. In order to provide many-to-many interaction when the number of participants is large, IP multicasting is a very good option for communication. IP multicasting provides scalability and efficient routing but does not provide the reliability that these multimedia applications may require. Though a lot of research has been done on reliable multicast transport protocols, it really seems that the only way of doing a reliable multicast is to build it for a given purpose like conference control in multimedia conferencing. This paper compares some of the available multicast transport protocols and analyses the most suitable features and functionalities provided by these protocols for a facet of conference control: floor control. The goal is to find or design a reliable multicast transport protocol which would scale to tens or hundreds of participants scattered across the Internet and which would deliver the control messages reliably.

Traditional mechanisms for service access, by mobile hosts across muhiple service domains, rely on the concept of a home location and cross domain authentication using roaming agreements. The reasons for requiring a home location are considered and it is argued that with recent developmenrs in elecn-onic payment schemes an alternative is possible. In an environment with many service providers, a wide range of services, and a diverse user population, roaming agreements based on a home location provide a far from ideal solution. An allernative “homeless ” mechanism based on the notion of tickets is proposed. The requirements and impacts of such a scheme are considered. The proposed scheme is described and anaiysed to show robusrness against specific problems. A description of a protogpe implemenration is included, together with ideas for firm-e work. 1.

IP Multicast, Lightweight Sessions and Application Level Framing provide guidelines by which multimedia conferencing tools can be designed, but they do not provide specific solutions. In this paper, we use these design principles to guide the design of a multicast based shared editor, and examine the consequences of taking a loose consistency approach to achieve good performance in the face of network failures and losses.

Group communication supports information transfer between a set of participants. It is becoming more and more relevant in distributed environments. For distributed or replicated data, it provides efficient communication without overloading the network. For some types of multimedia applications, it is the only way to control data transmission to group members. This paper surveys protocol functions and mechanisms for data transmission within a group, from multicast routing problems up to end-to-end multipoint transmission control. We provide a bibliography which is organized by topic.

This paper is about the evolution of the Internet from a simple data network into a true multiservice network that can support the emerging multimedia applications and their protocols with appropriate performance and costs. The real-time delivery and specific bandwidth requirements of these multimedia applications have created a need for an integrated services Internet in which traditional best effort datagram delivery can coexist with additional enhanced quality of service delivery classes. The integrated services Internet will be able to commit to meet bandwidth, packet loss, and delay specifications for individual data flows by using the resource reservation protocol together with appropriate packet forward scheduling policies.

Several new architectures have been developed for supporting multimedia applications such as digital video and audio. However, quality-of-service (QoS) routing is an important element that is still missing from these architectures. In this paper, we consider a number of issues in QoS routing. We first examine the basic problem of QoS routing, namely, finding a path that satisfies multiple constraints, and its implications on routing metric selection, and then present three path computation algorithms for source routing and for hop-by-hop routing.

Security issues in multicast communication have rarely been touched upon to date. We believe that wide-area multicast communication is at a substantially increased risk from specific security threats, compared with the same threats in unicast. This arises both from the lack of any form of effective group access control, and from the fact that multicast traffic traverses potentially many more communication links than does a single unicast communication, thereby creating more opportunity for a link attack. We discuss specific threats that are relevant to multicast, and explain why they are so. We propose security mechanisms specifically for multicast groups requiring safeguards that afford protection against some of these threats. More precisely, we propose a version of the IGMP protocol that can reliably enforce subnet-level group access control. We also describe a scalable mechanism to control multicast traffic in transit that can, for example, prevent a misbehaving source from causing undue congestion over the wide-area.>

This paper presents the Conference Control Channel Protocol (CCCP), a new scheme intended for controlling conferences ranging from small, tightly coupled meetings, to extremely large loosely coupled seminars. We describe the requirements of such a scheme, and present a framework for building systems that connect together new and existing applications.

No abstract available.

One of the central problems in one-to-many wide-area communications is forming the delivery tree - the collection of nodes and links that a multicast packet traverses. Significant problems remain to be solved in the area of multicast tree formation, the problem of scaling being paramount among these.In this paper we show how the current IP multicast architecture scales poorly (by scale poorly, we mean consume too much memory, bandwidth, or too many processing resources), and subsequently present a multicast protocol based on a new scalable architecture that is low-cost, relatively simple, and efficient. We also show how this architecture is decoupled from (though dependent on) unicast routing, and is therefore easy to install in an internet that comprises multiple heterogeneous unicast routing algorithms.

This paper examines burstiness and jitter in real-time communications. In this paper, we make so assumptions about the arrival patterns of the incoming traffic but characterize the traffic with two parameters. We assume that the synchronization process is adaptive, so that the traffic stream can be divided into smaller synchronization units. The jitter is defined with the delay experienced by the first packet in a synchronization unit as the target delay. We present the results on the relationship between burstiness and jitter, and on the upper bounds of burstiness and jitter.

The specification, design and implementation of a text-based multiway interactive conferencing program are presented. The system is described in three parts: the user interface, the distribution mechanism for users' contributions, and the floor control scheme. One specific system of floor control using the formal specification language Z is specified. The separation of the distributed operation of the exchange of speech in the conference from the floor control mechanism has led to the possibility of implementing an open floor conferencing system, and then adding floor control. The only operations necessary to add floor control are those required to allow or prevent use of a channel from each user to any other. The extra state incurred in the conference server processes is the 2-D matrix of user by user channels, with each entry showing allowed/disallowed.>

Under heavy and dynamic traffic, the SPF routing algorithm often suffers from wild oscillation and severe congestion, and results in degradation of the network performance. In this paper, we present a new routing algorithm (SPF-EE) which attempts to eliminate the problems associated with the SPF algorithm by providing alternate paths as emergency exits. With the SPF-EE algorithm, traffic is routed along the shortest-paths under normal condition. However, in the presence of congestion and resource failures, the traffic can be dispersed temporarily to alternate paths without route re-computation. Simulation experiments show that the SPF-EE algorithm achieves grater throughput, higher responsiveness, better congestion control and fault tolerance, and substantially improves the performance of routing in a dynamic environment.

This paper presents the design of a reliable multicast transport protocol. The aim of the protocol is to provide a service equivalent to a sequence of reliable sequential unicasts between a client and a number of servers, whilst using the broadcast nature of some networks to reduce both the number of packets transmitted and the overall time needed to collect replies.

The analysis of the test and measurement of TCP/IP performance over the Atlantic Packet Satellite Network (SATNET) is described. Both the methodology and tools as well as the results and their analysis are discussed. Because of the Internetwork nature of the environment, the tests were designed to allow the SATNET Measurement Taskforce to look at the effects of each component of the end-to-end path, e.g., local networks, gateways, SATNET, and protocol layers. Results are given for the IP service provided by SATNET as a function of offered load and for TCP behavior as a function of offered load and the underlying IP service.