We propose a privacy preserving mobile advertising system for in-app ad placement, that enables user profiling and targeted ads without revealing user interests to the mobile advertising companies. Our proposal relies on device-based user profiles, derived from app activity, on the use of Private Information Retrieval (PIR) to query ads database(s) for matching (to profile) ads, without the database(s) learning the content or the result of queries. We implement a Proof of Concept (POC) solution comprising critical system components for Android devices, including the profile builder and the PIR mechanism based on Percy++ library (ported to Android). We evaluate the practicality of selected PIR techniques in a mobile ads system using measured real world parameters. Overall, we show that a mobile PIR client can be effectively used for private advertising: for a single client connecting to a desktop PIR server, the Information theoretic (IT) and Hybrid PIR mechanisms allow close to real time ad retrieval. E.g., when querying a 1GB ad database for a block of 4 ads (total of 64KB), the ads are retrieved with a delay of around 2.5sec and utilising (for IT PIR) 1.25MB of data. The selected Computational PIR mechanism, however, introduces unacceptable overheads (the delay is of the order of 1300sec and 9.4GB of data is exchanged between the Android client and server for the same ad block). Further multi-client scalability tests indicate that, for all schemes, the server side is a performance bottleneck and, in addition to using commercial grade equipment, implementation enhancements including parallel processing would be necessary to have close to real time system responsiveness.

Smart devices are gaining popularity in our homes with the promise to make our lives easier and more comfortable. However, the increased deployment of such smart devices brings an increase in potential security risks. In this work, we propose an intrusion detection and mitigation framework, called IoT-IDM, to provide a network-level protection for smart devices deployed in home environments. IoT-IDM monitors the network activities of intended smart devices within the home and investigates whether there is any suspicious or malicious activity. Once an intrusion is detected, it is also capable of blocking the intruder in accessing the victim device on the fly. The modular design of IoT-IDM gives its users the flexibility to employ customized machine learning techniques for detection based on learned signature patterns of known attacks. Software-defined networking technology and its enabling communication protocol, OpenFlow, are used to realise this framework. Finally, a prototype of IoT-IDM is developed and the applicability and efficiency of proposed framework demonstrated through a real IoT device: a smart light bulb.

k-means++ seeding has become a de facto standard for hard clustering algorithms. In this paper, our first contribution is a two-way generalisation of this seeding, k-variates++, that includes the sampling of general densities rather than just a discrete set of Dirac densities anchored at the point locations, *and* a generalisation of the well known Arthur-Vassilvitskii (AV) approximation guarantee, in the form of a *bias+variance* approximation bound of the *global* optimum. This approximation exhibits a reduced dependency on the "noise" component with respect to the optimal potential — actually approaching the statistical lower bound. We show that k-variates++ *reduces* to efficient (biased seeding) clustering algorithms tailored to specific frameworks; these include distributed, streaming and on-line clustering, with *direct* approximation results for these algorithms. Finally, we present a novel application of k-variates++ to differential privacy. For either the specific frameworks considered here, or for the differential privacy setting, there is little to no prior results on the direct application of k-means++ and its approximation bounds — state of the art contenders appear to be significantly more complex and / or display less favorable (approximation) properties. We stress that our algorithms can still be run in cases where there is *no* closed form solution for the population minimizer. We demonstrate the applicability of our analysis via experimental evaluation on several domains and settings, displaying competitive performances vs state of the art.

The explosion in Internet-connected household devices, such as light-bulbs, smoke-alarms, power-switches, and webcams, is creating new vectors for attacking "smart-homes" at an unprecedented scale. Common perception is that smart-home IoT devices are protected from Internet attacks by the perimeter security offered by home routers. In this paper we demonstrate how an attacker can infiltrate the home network via a doctored smart-phone app. Unbeknownst to the user, this app scouts for vulnerable IoT devices within the home, reports them to an external entity, and modifies the firewall to allow the external entity to directly attack the IoT device. The ability to infiltrate smart-homes via doctored smart-phone apps demonstrates that home routers are poor protection against Internet attacks and highlights the need for increased security for IoT devices.

Recommender systems are increasingly becoming an integral part of on-line services. As the recommendations rely on personal user information, there is an inherent loss of privacy resulting from the use of such systems. While several works studied privacy-enhanced neighborhood-based recommendations, little attention has been paid to privacy preserving latent factor models, like those represented by matrix factorization techniques. In this paper, we address the problem of privacy preserving matrix factorization by utilizing differential privacy, a rigorous and provable privacy preserving method. We propose and study several approaches for applying differential privacy to matrix factorization, and evaluate the privacy-accuracy trade-offs offered by each approach. We show that input perturbation yields the best recommendation accuracy, while guaranteeing a solid level of privacy protection.

The increasing uptake of smart home appliances, such as lights, smoke-alarms, power switches, baby monitors, and weighing scales, raises privacy and security concerns at unprecedented scale, allowing legitimate and illegitimate entities to snoop and intrude into the family's activities. In this paper we first illustrate these threats using real devices currently available in the market. We then argue that as more such devices emerge, the attack vectors increase, and ensuring privacy/security of the house becomes more challenging. We therefore advocate that device-level protections be augmented with network-level security solutions, that can monitor network activity to detect suspicious behavior. We further propose that software defined networking technology be used to dynamically block/quarantine devices, based on their network activity and on the context within the house such as time-of-day or occupancy-level. We believe our network-centric approach can augment device-centric security for the emerging smart-home.

The increasing heterogeneity and asymmetry in wireless network environments makes QoS guarantees in terms of delays and throughput a challenging task. In this paper, we study a novel scheduling algorithm for multipath transport called Delay Aware Packet Scheduling (DAPS) which aims to reduce the receiver's buffer blocking time considered as a main parameter to enhance the QoS in wireless environments. We develop an analytical model of maximum receiver's buffer blocking time and extend the DAPS algorithm considering implementation issues. Performance evaluations based on ns-2 simulations highlight the enhanced QoS that DAPS can provide. With reference to the classical multipath transport protocol CMT-SCTP, we observe a significant reductions of the receiver's buffer occupancy, down by 77%, and the application delay, down by 63%.

Recommender systems are widely used by online retailers to promote products and content that are most likely to be of interest to a specific customer. In such systems, users often implicitly or explicitly rate products they have consumed, and some form of collaborative filtering is used to find other users with similar tastes to whom the products can be recommended. While users can benefit from more targeted and relevant recommendations, they are also exposed to greater risks of privacy loss, which can lead to undesirable financial and social consequences. The use of obfuscation techniques to preserve the privacy of user ratings is well studied in the literature. However, works on obfuscation typically assume that all users uniformly apply the same level of obfuscation. In a heterogeneous environment, in which users adopt different levels of obfuscation based on their comfort level, the different levels of obfuscation may impact the users in the system in a different way. In this work we consider such a situation and make the following contributions: (a) using an offline dataset, we evaluate the privacy-utility trade-off in a system where a varying portion of users adopt the privacy preserving technique. Our study highlights the effects that each user's choices have, not only on their own experience but also on the utility that other users will gain from the system, and (b) we propose Privacy Canary, an interactive system that enables users to directly control the privacy-utility trade-off of the recommender system to achieve a desired accuracy while maximizing privacy protection, by probing the system via a private (i.e., undisclosed to the system) set of items. We evaluate the performance of our system with an off-line recommendations dataset, and show its effectiveness in balancing a target recommender accuracy with user privacy, compared to approaches that focus on a fixed privacy level.

Network outages are an important issue for Internet Service Providers (ISPs) and, more generally, online service providers, as they can result in major financial losses and negatively impact relationships with their customers. Troubleshooting network outages is a complex and time-consuming process. Network administrators are overwhelmed with large volumes of monitoring data and are limited to using very basic tools for debugging, e.g., ping and traceroute. Intelligent correlation of measurements from different Internet locations is very useful for analyzing the root cause of outages. However, correlating measurements of user traffic across domains is largely avoided as it raises privacy concerns. A possible solution is secure multi-party computation (MPC), a set of cryptographic methods that enable a number of parties to aggregate data in a privacy-preserving manner. In this work, we describe a novel system that helps diagnose network outages by correlating passive measurements from multiple ISPs in a privacy-preserving manner. We first show how MPC can be used to compute the scope (local, global, or semi-global) and severity (number of affected hosts) of network outages. To meet near-real-time monitoring guarantees, we then present an efficient protocol for MPC multiset union that uses counting Bloom filters (CBF) to drastically accelerate MPC comparison operations. Finally, we demonstrate the utility of our scheme using real-world traffic measurements from a national ISP and we discuss the trade-offs of the CBF-based computation.

Sensors are commonly deployed in hostile environment, and consequently a number of research works have focused on data aggregation schemes designed to be tolerant to attacks on sensor nodes. In parallel, schemes ensuring the confidentiality of sensor data have been proposed to address the emerging privacy concerns. We note that resilience against tampering attacks requires access to the sensor node's data, while in privacy-preserving systems this data must remain confidential. In this work, we aim to reconcile these two seemingly conflicting objectives. We present a novel private and resilient aggregation system, in which an aggregator combines the data collected from sensor nodes and forwards the resulting sum to an analyst. Our scheme protects the privacy of the users from both honest-but-curious aggregator and analyst, while enabling the filtering of fake data values using a Private Range Test protocol.

Performance of transport protocols on lossy links is a well researched topic, however there are only a few proposals which make use of the opportunities presented by the use of erasure coding within the transport layer, particularly in the multipath transport protocol context. In this paper, we investigate the improvements in the performance of multipath SCTP transport protocol brought by the novel integration of the on-the-fly erasure code into the congestion control and reliability mechanisms of CMT-SCTP. Our contributions include: integration of transport protocol and erasure codes with regards to congestion control and reliability mechanisms; proposal for a variable retransmission delay parameter in the sender (aRTX) adjustment; evaluation of the performance of CMT-SCTP with erasure coding with simulations. Our results show that we can achieve from 10% to 80% improvements in application goodput under lossy multipath network conditions without a significant penalty i.e. with a minimal (10%) overhead due to the encoding-decoding process. We further evaluate the performance of video streaming using an equivalent of partially reliable CMT-SCTP with erasure coding and again demonstrate a solid performance improvement for our proposal, compared to CMT-SCTP. Finally, we provide an analytical evaluation of CMT-SCTP with erasure codes and validate this with simulation results.

In this paper, we investigate how the concept of differential privacy can be applied to Intelligent Transportation Systems (ITS), focusing on protection of Floating Car Data (FCD) stored and processed in central Traffic Data Centers (TDC). We illustrate an integration of differential privacy with privacy policy languages and policy-enforcement frameworks like the PRECIOSA PeRA architecture. Next, we identify differential privacy mechanisms to be integrated within the policy-enforcement framework and provide guidelines for the calibration of parameters to ensure specific privacy guarantees, while still supporting the level of accuracy required for ITS applications. We also discuss the challenges that the support of user-level differential privacy presents and outline a potential solution. As a result, we show that differential privacy could be put to practical use in ITS to enable strong protection of users' personal data.

Greedy forwarding is an efficient and scalable geographic routing algorithm for wireless networks. To guarantee the success of greedy forwarding, many research efforts assign virtual coordinates to nodes to obtain a greedy embedding of the network. Different from these existing efforts, this paper presents an approach that enables greedy forwarding to succeed in directional wireless networks by selecting links in the network instead of assigning virtual coordinates to the nodes. Specifically, this paper studies the following problem: given a set of nodes on the Euclidean plane, how can we add a minimum number of point-to-point links, such that the greedy forwarding algorithm succeeds on the resulting network. The motivation for studying this problem is that each point-to-point link in directional wireless networks is realized by a pair of directional antennas, so minimizing the number of links will reduce the network installation cost. This paper first presents the properties of the graphs supporting greedy forwarding, and then solves the above problem optimally by Integer Linear Programming and also sub-optimally by a polynomial-time 3-approximation algorithm. Finally, this paper compares the polynomial-time algorithm with the optimal solution, showing that the polynomial-time algorithm can actually generate within 1.1 times the number of links found by the optimal solution in most cases.

We present a solution to evaluate the performance of transport protocols as a function of link layer reliability schemes (i.e. ARQ, FEC and Hybrid ARQ) applied to satellite physical layer traces. As modelling such traces is complex and may require approximations, the use of real traces will minimise the potential for erroneous performance evaluations resulting from imperfect models. Our Trace Manager Tool (TMT) produces the corresponding link layer output, which is then used within the ns-2 network simulator via the additionally developed ns-2 interface module. We first present the analytical models for the link layer with bursty erasure packets and for the link layer reliability mechanisms with bursty erasures. Then, we present details of the TMT tool and our validation methodology, demonstrating that the selected performance metrics (recovery delay and throughput efficiency) exhibit a good match between the theoretical results and those obtained with TMT. Finally, we present results showing the impact of different link layer reliability mechanisms on the performance of TCP Cubic transport layer protocol.

We consider stored content being streamed to a resource-poor device (such as a sensor node or a mobile phone), and address the issue of authenticating such content in realtime at the receiver. Per-packet digital signatures incur high computational cost, while per-block signatures impose high delays. A Merkle hash tree combines the benefits of the two by having a single signature per-block (at the root of the tree), while allowing immediate per-packet verification by following a hash-path logarithmic in the number of packets. In this paper we explore how the structure of the Merkle tree can be adapted to improve playback performance for streaming content. We make three specific contributions: First, we develop a new unbalanced authentication tree structure called the α-leaf tree that is a generalisation of the Merkle tree. We derive several key properties of this tree, highlighting the impact of the imbalance parameter α. Second, we present a theoretical model to quantify the benefits of our unbalanced tree structure in reducing startup delays for streaming applications by optimally readjusting the burden of authentication across packets. Third, we validate via simulation the suitability of our scheme to two representative applications, namely audio streaming to a low-cost sensor device and video streaming to a mobile phone, and demonstrate that startup delays can be reduced without affecting stall rates. We believe our authentication tree structure is of importance both theoretically, as a generalisation of the Merkle hash tree, as well as practically, for applications requiring real-time verification of streaming content.

Active service discovery in Wi-Fi involves wireless stations broadcasting their Wi-Fi fingerprint, i.e. the SSIDs of their preferred wireless networks. The content of those Wi-Fi fingerprints can reveal different types of information about the owner. We focus on the relation between the fingerprints and the links between the owners. Our hypothesis is that social links between devices owners can be identified by exploiting the information contained in the fingerprint. More specifically we propose to consider the similarity between fingerprints as a metric, with the underlying idea: similar fingerprints are likely to be linked. We first study the performances of several similarity metrics on a controlled dataset and then apply the designed classifier to a dataset collected in the wild. Finally we discuss how Wi-Fi fingerprint can reveal informations on the nature of the links between users. This study is based on a dataset collected in Sydney, Australia, composed of fingerprints corresponding to more than 8000 devices.

Video streaming for heterogeneous types of devices, where nodes have different devices characteristics in terms of computational capacity and display, is usually handled by encoding the video with different qualities. This is not well suited for Peer-To-Peer (P2P) systems, as a single peer group can only share content of the same quality, thus limiting the peer group size and efficiency. To address this problem, several existing works propose the use of Multiple Descriptions Coding (MDC). The concept of this type of video codec is to split a video in a number of descriptions which can be used on their own, or aggregated to improve the global quality of the video. Unfortunately existing MDC codes are not flexible, as the video is split in a defined number of descriptions. In this paper, we focus on the practical feasibility of using a Fountain MDC code with properties similar to existing Fountain erasure codes, including the ability to create any number of descriptions when needed (on the fly). We perform simulations using selected pictures to assess the feasibility of using these codes, knowing that they should improve the availability of the video pieces in a P2P system and hence the video streaming quality. We observe that, although this idea seems promising, the evaluated benefits, demonstrated by the PSNR values, are limited when used in a real P2P video streaming system.

We propose a solution to mitigate the performance degradation and corresponding Quality of Experience (QoE) reduction caused by packet reordering for multimedia applications which utilise unreliable transport protocols like the Datagram Congestion Control Protocol (DCCP). We analytically derive the optimum buffer size based on the applications data rate and the maximum delay tolerated by the multimedia application. We propose a dynamically adjustable buffer in the transport protocol receiver which uses this optimum buffer size. We demonstrate, via simulation results, that our solution reduces the packet loss rate, increases the perceived bandwidth and does not increase jitter in the received applications packets while still being within the application's delay limits, therefore resulting in an increased QoE of multimedia applications.

We present Xstream-x264: a real-time cross-layer video streaming technique implemented within a well known open-source H.264 video encoder tool x264 for on-line video quality evaluation experiments. Xstream-x264 uses the transport protocol provided indication of the available data rate for corresponding adjustments in the video encoder. We discuss the design, implementation and the quality evaluation methodology utilized with our tool. We demonstrate via experimental results that the streaming video quality greatly improves with the presented cross-layer approach both in terms of lost frame count and the objective video quality metrics Peak Signal to Noise Ratio (PSNR).

We consider the problem of network selection and flow distribution for a multihomed mobile device. We argue the benefits of a holistic approach which considers user- and application-centric metrics such as quality, energy consumption and monetary cost, rather than the commonly used network-centric metrics. We thus introduce the multihomed flow management problem which combines network selection, flow distribution and application flow awareness. We formulate it as a constrained optimisation problem and compare it to commonly used techniques: single network selection and load balancing. For selected interactive applications, we use empirical network measurements to evaluate the optimal solutions obtained by the three approaches. We show that, by exploiting the flexibility of application parameters, it is possible to achieve the potentially conflicting goals of maintaining high application quality while reducing both the power consumption and cost of network use.

With the growing popularity and capabilities of mobile devices, peer-to-peer networking among such devices is increasingly of interest for mobile content sharing. One of the major challenges in practical use of Mobile Peer-to-Peer networks (MP2P) is the trust among peers. Traditionally, solutions in the state of the art have focused on a peer's past experience in evaluating trust of other peers, based on direct interactions. Previously unknown peers (with no history of direct interactions) are assessed based on third party recommendations, yet again requiring a peer to evaluate and find trustworthy recommenders. This reveals the fundamental need to find peers with honest intentions before any interaction. It becomes challenging when no known peers are in the vicinity, which is highly likely in an MP2P scenario. For a general mobile user, the probability of encountering trustworthy peers in particular situations or environmental contexts may be higher than in other contexts, e.g. in office than on the road while traveling. Further, observed peers which are co-located over a number of environmental contexts may have more in common and thus resulting a higher mutual trust. These facts can be utilized to enrich the trust derivation process in a decentralized manner. In this paper, we propose a generalized and a novel distributed mechanism to estimate the trust for peers using their encounter history in different environmental contexts, and a way to prioritize contexts depending on the level of association with them. When evaluated against real user data of the reality mining dataset, the results of the proposed mechanism show a significantly improved accuracy of trust evaluation compared to the state of the art.

We propose a novel trust and probabilistic node selection mechanism for content distribution in mobile ad hoc networks, which aims to achieve trustworthy node selection and to preserve mobile node resources. The proposed mechanism is evaluated against selected alternative trust schemes, with the results showing that our proposal achieves its goals.

The currently proposed anonymous routing mechanisms for Mobile Ad hoc Networks enable network entities to anonymously and securely communicate with each other. However, protocols that provide a high level of anonymity generally have poor scalability due to delays and overhead introduced by cryptographic operations, while other approaches sacrifice anonymity to achieve better performance. In this paper, we propose a novel anonymous routing protocol that provides improved anonymity and security while achieving similar or better performance, as compared to existing proposals. Our proposal achieves anonymity using a novel efficient solution for invisible implicit addressing based on keyed hash chain and security via a novel application of one-to-many Diffie-Hellman mechanism, used to exchange keys for symmetric encryption. The final contribution includes a mechanism to facilitate selection of a trusted route by verifying connections between intermediate nodes. We demonstrate the benefits of our proposal in comparison with previous approaches using analysis and simulation.

We present data acceleration and reduction technology (DART), a new rate based congestion control and scheduling mechanism with integrated data compression. DART has been implemented in the 7-ip satellite gateway products and is based on SCPS-TP framework. We evaluate the performance of this scheme on two live commercially available satellite systems, IPSTAR and Inmarsat BGAN and compare it to the performance of a number of TCP flavors previously proposed for use on satellite links. We show that on congested satellite links the proposed scheme outperforms the established techniques. We additionally highlight the benefits of data compression when fully integrated within the transport protocol. Finally, we demonstrate fairness of the proposed scheme to other flows.

We present experimental results for the performance of selected voice codecs using the Datagram Congestion Control Protocol (DCCP) with TCP-Friendly Rate Control (TFRC) congestion control mechanism over a satellite link. We evaluate the performance of both constant and variable data rate speech codecs (G.729, G.711 and Speex) for a number of simultaneous calls, using the ITU E-model and identify problem areas and potential for improvement. Our experiments are done on a commercial satellite service using a data stream generated by a VoIP application, configured with selected voice codecs and using the DCCP/CCID4 Linux implementation. We analyse the sources of packet losses which are a main contributor to reduced voice quality when using CCID4 and additionally analyse the effect of jitter which is one of the crucial parameters contributing to VoIP quality and has, to the best of our knowledge, not been considered previously in the published DCCP performance results. We propose modifications to the CCID4 algorithm and demonstrate how these improve the VoIP performance, without the need for additional link information other than what is already monitored by CCID4 (which is the case for Quick-Start). We also demonstrate the fairness of the proposed modifications to other flows. We identify the additional benefit of DCCP when used in VoIP admission control mechanisms and draw conclusions about the advantages and disadvantages of the proposed DCCP/CCID4 congestion control mechanism for use with VoIP applications.

We present a novel mechanism for adjusting the combination of the number of simultaneous VoIP calls and voice quality, based on the monitored WiMax link parameters. We have implemented this mechanism which combines call admission control and setting of variable voice codec rate (and corresponding quality) dynamically in the Asterix PBX using the Speex voice codec. The mechanism includes dynamic parameter adjustments of currently active VoIP calls, with the capability to change quality on a per voice frame basis. It additionally limits acceptance of new calls until there is sufficient capacity to maintain a pre-defined minimum quality for the existing calls. We have also implemented the wireless link capacity estimator, which, based on the parameters measured in the receiver, estimates the data rate currently available on the wireless link. Our implementation is for a pre-WiMax service offered by UNWIRED in Australia. We show that the proposed cross layer optimisation enables a larger number of calls with improved voice quality, compared to a fixed scheme. We are currently working with a certified WiMax equipment supplier and we plan to extend the scheme to full WiMax implementation.

WIMAX (Worldwide Interoperability for Microwave Access) is a promising new networking technology that potentially offers high speed and wide area wireless access services that complement consistently the 3G and WiFi access networks capabilities. The standard proposes an adaptive modulation scheme which allows WiMax nodes to communicate from various modulation coding schemes according to the link quality. However, the standard does not define a detailed link adaptation algorithm and currently, the most largely used modulation adaptation technique is based on a channel quality lookup table. We argue that this method is not able to make the best adaptation decisions and delivers a sub-optimal goodput in numerous communication contexts. In this paper, we propose a novel cross layer based modulation adaptation mechanism which incorporates the use of adaptive erasure code with the physical layer information to significantly improve the goodput and transmission efficiency. Simulation results show that our proposal adapts more efficiently to real environments and achieves a significant gain on the goodput delivered to mobile nodes.

In the context of mobile and pervasive networking, it is not uncommon to experience frequent loss of connectivity. Congestion control algorithms usually mistake resulting packets losses for congestion events and unnecessarily reduce their network usage. We propose an enhancement to the TCP-Friendly Rate Control (TFRC) protocol to overcome this issue. Our contribution proposes to suspend the data transfer, in a way similar to Freeze-TCP, when an upcoming disconnection is predicted. Furthermore, a probing mechanism is introduced to enable fast adaptation to new network conditions. We present a description of the additional freezing and resuming mechanisms. This proposal has been implemented in ns-2. Simulation results are thus compared to the regular behavior and show encouraging improvements.

We present arguments to support the co-existence of two or more mobility management schemes, and detail a novel approach to integrate the mobility and multi-homing support provided by a higher level mobility mechanism, the host identity protocol (HIP), with a lower level mobility mechanism, network mobility (NEMO, i.e. mobile IPv6 enabled routers). Our design integrates a context aware handoff system that triggers the dynamic switching of the HIP host identity binding between a private address managed by a mobile router and a topologically correct address, in order to simultaneously take advantage of the efficient handover offered by vehicular networks with NEMO support, and the infrastructure-less operability of HIP when mobile routers become unavailable. We discuss implementation and detail how our scheme uses context cues such as vehicle speed to switch between NEMO and HIP mobility handling. We present results of emulation experiments that support our design

Advances in ubiquitous computing applications depend heavily on developments in supporting technologies, including network communications. Recent research in Internet addressing has pointed towards a need for a more generic approach to the definition of endpoint identity and a change in the traditional communication model. This change defines one or more thin layers of resolution between network, transport, or application level identifiers. In this paper we advocate the concept of endpoint identity location decoupling in the context of ubiquitous computing. We provide an analysis of the general network security issues affecting ubiquitous computing in terms of the identity/location decoupled naming scheme approach.

Abstract — The proliferation of the increasing number of devices and technologies has brought about the extension of the concept of roaming users to roaming networks. This has inspired many researchers to investigate optimal mechanisms to enable communications for such roaming networks. Within the European 6th Framework Ambient Networks Project, we identify such roaming networks as dynamic Routing Groups, which consist of a number of different types of nodes with different capabilities. External communications from nodes within the routing group can be done via selected Gateway nodes. In this paper we present the Gateway Selection Architecture (GSA) which provides support for Gateway identification, management and selection for nodes within a RG. We describe the benefits of this architecture and compare it to other known approaches.