In a wireless network, mobile nodes (MNs) repeatedly perform tasks such as layer 2 (L2) handoff, layer 3 (L3) handoff and authentication. These tasks are critical, particularly for real-time applications such as VoIP. We propose a novel approach, namely Cooperative Roaming (CR), in which MNs can collaborate with each other and share useful information about the network in which they move. We show how we can achieve seamless L2 and L3 handoffs regardless of the authentication mechanism used and without any changes to either the infrastructure or the protocol. In particular, we provide a working implementation of CR and show how, with CR, MNs can achieve a total L2+L3 handoff time of less than 16 ms in an open network and of about 21 ms in an IEEE 802.11i network. We consider behaviors typical of IEEE 802.11 networks, although many of the concepts and problems addressed here apply to any kind of mobile network.
More recently, cooperative approaches have been proposed in the network community. , @cite_2 show how cooperation amongst MNs can be beneficial for all the MNs in the network in terms of bit-rate, coverage and throughput. Each MN builds a table in which possible helpers for that MN are listed. If an MN has a poor link with the AP and its bit-rate is low, it sends packets to the helper who relays them to the AP. The advantage in doing this is that the link from the MN to the helper and from the helper to the AP is a high bit-rate link. In this way the MN can use two high bit-rate links via the helper instead of the low bit-rate one directly to the AP.
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In multiuser MIMO (MU-MIMO) networks, the optimal bit rate of a user is highly dynamic and changes from one packet to the next. This breaks traditional bit rate adaptation algorithms, which rely on recent history to predict the best bit rate for the next packet. To address this problem, we introduce TurboRate, a rate adaptation scheme for MU-MIMO LANs. TurboRate shows that clients in a MU-MIMO LAN can adapt their bit rate on a per-packet basis if each client learns two variables: its SNR when it transmits alone to the access point, and the direction along which its signal is received at the AP. TurboRate also shows that each client can compute these two variables passively without exchanging control frames with the access point. A TurboRate client then annotates its packets with these variables to enable other clients to pick the optimal bit rate and transmit concurrently to the AP. A prototype implementation in USRP-N200 shows that traditional rate adaptation does not deliver the gains of MU-MIMO WLANs, and can interact negatively with MU-MIMO, leading to low throughput. In contrast, enabling MU-MIMO with TurboRate provides a mean throughput gain of 1.7x and 2.3x, for 2-antenna and 3-antenna APs respectively. In wireless local area networks, often a station can potentially associate with more than one access point (AP). Therefore, a relevant question is which AP to select 'best' from a list of candidate ones. In IEEE 802.11, the user simply associates to the AP with the strongest received signal strength. However, this may result in a significant load imbalance between several APs. Moreover, the multi-rate flexibility provided by several IEEE 802.11 variants can cause low bit rate stations to negatively affect high bit rate ones and consequently degrade the overall network throughput. This paper investigates the various aspects of 'best' AP selection for IEEE 802.11 systems. In detail, we first derive a new decision metric which can be used for AP selection. Using this metric, we propose two new selection mechanisms which are decentralised in the sense that the decision is performed by each station, given appropriate status information of each AP. In fact, only few bytes of status information have to be added to the Beacon and Probe Response frames which does not impose significant overhead. We show that our mechanism improves mean quality of service of all stations and better utilises network resources compared to the conventional one implemented today in IEEE 802.11 devices. Also, the schemes are appealing in terms of stability and provide their performance improvement even for denser or lighter network configurations. A generalization of the Gaussian dirty-paper problem to a multiple access setup is considered. There are two additive interference signals, one known to each transmitter but none to the receiver. The rates achievable using Costa's strategies (i.e. by a random binning scheme induced by Costa's auxiliary random variables) vanish in the limit when the interference signals are strong. In contrast, it is shown that lattice strategies ("lattice precoding") can achieve positive rates independent of the interferences, and in fact in some cases - which depend on the noise variance and power constraints - they are optimal. In particular, lattice strategies are optimal in the limit of high SNR. It is also shown that the gap between the achievable rate region and the capacity region is at most 0.167 bit. Thus, the dirty MAC is another instance of a network setup, like the Korner-Marton modulo-two sum problem, where linear coding is potentially better than random binning. Lattice transmission schemes and conditions for optimality for the asymmetric case, where there is only one interference which is known to one of the users (who serves as a "helper" to the other user), and for the "common interference" case are also derived. In the former case the gap between the helper achievable rate and its capacity is at most 0.085 bit. We consider the design of a scheduling policy for video streaming in a wireless network formed by several users and helpers (e.g., base stations). In such networks, any user is typically in the range of multiple helpers. Hence, an efficient policy should allow the users to dynamically select the helper nodes to download from and determine adaptively the quality level of the requested video segment. In order to obtain a tractable formulation, we follow a “divide and conquer” approach. First, we formulate a network utility maximization (NUM) problem where the network utility function is a concave and component-wise nondecreasing function of the time-averaged users' requested video quality index, and maximization is subject to the stability of all queues in the system. Second, we solve the NUM problem by using a Lyapunov drift plus penalty approach, obtaining a dynamic adaptive scheme that decomposes into two building blocks: 1) adaptive video quality and helper selection (run at the user nodes); and 2) dynamic allocation of the helper-to-user transmission rates (run at the help nodes). Our solution provably achieves NUM optimality in a strong per-sample path sense (i.e., without assumptions of stationarity and ergodicity). Third, we observe that, since all queues in the system are stable, all requested video chunks shall be eventually delivered. Fourth, in order to translate the requested video quality into the effective video quality at the user playback, it is necessary that the chunks are delivered within their playback deadline. This requires that the largest delay among all queues at the helpers serving any given user is less than the pre-buffering time of that user at its streaming session startup phase. In order to achieve this condition with high probability, we propose an effective and decentralized (albeit heuristic) scheme to adaptively calculate the pre-buffering and re-buffering time at each user. In this way, the system is forced to work in the “smooth streaming regime,” i.e., in the regime of very small playback buffer underrun rate. Through simulations, we evaluate the performance of the proposed algorithm under realistic assumptions of a network with densely deployed helper and user nodes, including user mobility, variable bit-rate video coding, and users joining or leaving the system at arbitrary times. Video on-demand streaming from Internet-based servers is becoming one of the most important services offered by wireless networks today. In order to improve the area spectral efficiency of video transmission in cellular systems, small cells heterogeneous architectures (e.g., femtocells, WiFi off-loading) are being proposed, such that video traffic to nomadic users can be handled by short-range links to the nearest small cell access points (referred to as "helpers"). As the helper deployment density increases, the backhaul capacity becomes the system bottleneck. In order to alleviate such bottleneck we propose a system where helpers with low-rate backhaul but high storage capacity cache popular video files. Files not available from helpers are transmitted by the cellular base station. We analyze the optimum way of assigning files to the helpers, in order to minimize the expected downloading time for files. We distinguish between the uncoded case (where only complete files are stored) and the coded case, where segments of Fountain-encoded versions of the video files are stored at helpers. We show that the uncoded optimum file assignment is NP-hard, and develop a greedy strategy that is provably within a factor 2 of the optimum. Further, for a special case we provide an efficient algorithm achieving a provably better approximation ratio of @math , where @math is the maximum number of helpers a user can be connected to. We also show that the coded optimum cache assignment problem is convex that can be further reduced to a linear program. We present numerical results comparing the proposed schemes. Video on-demand streaming from Internet-based servers is becoming one of the most important services offered by wireless networks today. In order to improve the area spectral efficiency of video transmission in cellular systems, small cells heterogeneous architectures (e.g., femtocells, WiFi off-loading) are being proposed, such that video traffic to nomadic users can be handled by short-range links to the nearest small cell access points (referred to as “helpers”). As the helper deployment density increases, the backhaul capacity becomes the system bottleneck. In order to alleviate such bottleneck we propose a system where helpers with low-rate backhaul but high storage capacity cache popular video files. Files not available from helpers are transmitted by the cellular base station. We analyze the optimum way of assigning files to the helpers, in order to minimize the expected downloading time for files. We distinguish between the uncoded case (where only complete files are stored) and the coded case, where segments of Fountain-encoded versions of the video files are stored at helpers. We show that the uncoded optimum file assignment is NP-hard, and develop a greedy strategy that is provably within a factor 2 of the optimum. Further, for a special case we provide an efficient algorithm achieving a provably better approximation ratio of 1-(1-1 d )d, where d is the maximum number of helpers a user can be connected to. We also show that the coded optimum cache assignment problem is convex that can be further reduced to a linear program. We present numerical results comparing the proposed schemes. We show that the feasibility of a system of m linear inequalities over the cone of symmetric positive semidefinite matrices of order n can be tested in mn^O(min m,n^2 ) arithmetic operations with ln^O(min m,n^2 )-bit numbers, where l is the maximum binary size of the input coefficients. We also show that any feasible system of dimension (m,n) has a solution X such that log||X|| ≤ ln^O(min m,n^2 ). We consider secure multi-terminal source coding problems in the presence of a public helper. Two main scenarios are studied: 1) source coding with a helper where the coded side information from the helper is eavesdropped by an external eavesdropper and 2) triangular source coding with a helper where the helper is considered as a public terminal. We are interested in how the helper can support the source transmission subject to a constraint on the amount of information leaked due to its public nature. We characterize the tradeoff among transmission rate, incurred distortion, and information leakage rate at the helper eavesdropper in the form of the rate-distortion-leakage region for various classes of problems. Scavenging the idling computation resources at the enormous number of mobile devices, ranging from small IoT devices to powerful laptop computers, can provide a powerful platform for local mobile cloud computing. The vision can be realized by peer-to-peer cooperative computing between edge devices, referred to as co-computing . This paper exploits the non-causal helper’s CPU-state information to design energy-efficient co-computing policies for scavenging time-varying spare computation resources at peer mobiles. Specifically, we consider a co-computing system where a user offloads computation of input data to a helper. The helper controls the offloading process for the objective of minimizing the user’s energy consumption based on a predicted helper’s CPU-idling profile that specifies the amount of available computation resource for co-computing. Consider the scenario that the user has one-shot input-data arrival and the helper buffers offloaded bits. The problem for energy-efficient co-computing is formulated as two sub-problems: the slave problem corresponding to adaptive offloading and the master one to data partitioning. Given a fixed offloaded data size, the adaptive offloading aims at minimizing the energy consumption for offloading by controlling the offloading rate under the deadline and buffer constraints. By deriving the necessary and sufficient conditions for the optimal solution, we characterize the structure of the optimal policies and propose algorithms for computing the policies. Furthermore, we show that the problem of optimal data partitioning for offloading and local computing at the user is convex, admitting a simple solution using the sub-gradient method. Finally, the developed design approach for co-computing is extended to the scenario of bursty data arrivals at the user accounting for data causality constraints. Simulation results verify the effectiveness of the proposed algorithms. We introduce a modified matching pursuit algorithm, called fast ridge pursuit, to approximate N-dimensional signals with M Gaussian chirps at a computational cost O(MN) instead of the expected O(MN sup 2 logN). At each iteration of the pursuit, the best Gabor atom is first selected, and then, its scale and chirp rate are locally optimized so as to get a "good" chirp atom, i.e., one for which the correlation with the residual is locally maximized. A ridge theorem of the Gaussian chirp dictionary is proved, from which an estimate of the locally optimal scale and chirp is built. The procedure is restricted to a sub-dictionary of local maxima of the Gaussian Gabor dictionary to accelerate the pursuit further. The efficiency and speed of the method is demonstrated on a sound signal. This article describes the resource-and system-building efforts of an 8-week Johns Hopkins University Human Language Technology Center of Excellence Summer Camp for Applied Language Exploration (SCALE-2009) on Semantically Informed Machine Translation (SIMT). We describe a new modality negation (MN) annotation scheme, the creation of a (publicly available) MN lexicon, and two automated MN taggers that we built using the annotation scheme and lexicon. Our annotation scheme isolates three components of modality and negation: a trigger (a word that conveys modality or negation), a target (an action associated with modality or negation), and a holder (an experiencer of modality). We describe how our MN lexicon was semi-automatically produced and we demonstrate that a structure-based MN tagger results in precision around 86 (depending on genre) for tagging of a standard LDC data set. We apply our MN annotation scheme to statistical machine translation using a syntactic framework that supports the inclusion of semantic annotations. Syntactic tags enriched with semantic annotations are assigned to parse trees in the target-language training texts through a process of tree grafting. Although the focus of our work is modality and negation, the tree grafting procedure is general and supports other types of semantic information. We exploit this capability by including named entities, produced by a pre-existing tagger, in addition to the MN elements produced by the taggers described here. The resulting system significantly outperformed a linguistically naive baseline model (Hiero), and reached the highest scores yet reported on the NIST 2009 Urdu-English test set. This finding supports the hypothesis that both syntactic and semantic information can improve translation quality. This paper describes the design and implementation of a novel 802.11-based self-organizing hierarchical ad-hoc wireless network (SOHAN), and presents some initial experimental results obtained from a proof-of-concept prototype. The proposed network has a three-tier hierarchy consisting of low-power mobile nodes (MNs) at the lowest layer, forwarding nodes (FNs) with higher power and multi-hop routing capability at the middle layer, and wired access points (APs) without power constraints at the highest layer. Specifics of new protocols used for bootstrapping, node discovery, and multi-hop routing are presented, and the overall operation of the complete hierarchical ad-hoc network is explained. A prototype implementation of the SOHAN network is outlined in terms of major hardware and software components, and initial experimental results are given. Abstract An adaptive choice of the sample mean ¯xn or the sample median mn is proposed for estimating the center of a symmetric distribution. This choice becomes correct as n → ∞, and in simulation results for finite n it is almost as good as the better of ¯xn and mn. A new model of multi-party secret key agreement is proposed, in which one terminal called the communicator can transmit public messages to other terminals before all terminals agree on a secret key. A single-letter characterization of the achievable region is derived in the stationary memoryless case. The new model generalizes some other (old and new) models of key agreement. In particular, key generation with an omniscient helper is the special case where the communicator knows all sources, for which we derive a zero-rate one-shot converse for the secret key per bit of communication. The issue of routing and maintaining packets between mobile nodes (MNs) in the mobile ad hoc networks (MANETs) has always been a challenge; i.e. encountering broadcast storm under high node density, geographically constrained broadcasting of a service discovery message and local maximum problem under low node density. This requires an efficient design and development of a ‘light-weight’ routing algorithm which can be handled by those GPS equipped devices. Our proposed protocol, Local Area Network Dynamic routing (LANDY) uses a localized routing technique which combines an unique locomotion prediction method and velocity information of MNs to route packets, which is capable of improving the routing performance in advanced mobility scenarios by reducing the control overhead. Simulations have been carried out with randomising the mobility speed and number of nodes and the preliminary results illustrate that LANDY's performance is better compared to other position based routing protocols. This paper establishes the utility of user cooperation in facilitating secure wireless communications. In particular, the four-terminal relay-eavesdropper channel is introduced and an outer-bound on the optimal rate-equivocation region is derived. Several cooperation strategies are then devised and the corresponding achievable rate-equivocation region are characterized. Of particular interest is the novel noise-forwarding (NF) strategy, where the relay node sends codewords independent of the source message to confuse the eavesdropper. This strategy is used to illustrate the deaf helper phenomenon, where the relay is able to facilitate secure communications while being totally ignorant of the transmitted messages. Furthermore, NF is shown to increase the secrecy capacity in the reversely degraded scenario, where the relay node fails to offer performance gains in the classical setting. The gain offered by the proposed cooperation strategies is then proved theoretically and validated numerically in the additive white Gaussian noise (AWGN) channel. Binary maximum distance separable (MDS) array codes contain @math information columns and @math parity columns in which each entry is a bit that can tolerate @math arbitrary erasures. When a column in an MDS code fails, it has been proven that we must download at least half of the content from each helper column if @math columns are selected as the helper columns. If the lower bound is achieved such that the @math helper columns can be selected from any @math surviving columns, then the repair is an optimal repair. Otherwise, if the lower bound is achieved with @math specific helper columns, the repair is a weak-optimal repair. This paper proposes a class of binary MDS array codes with @math and @math that asymptotically achieve weak-optimal repair of an information column with @math helper columns. We show that there exist many encoding matrices such that the corresponding binary MDS array codes can asymptotically achieve weak-optimal repair for repairing any information column. We show how to store a compressed two-dimensional array such that, if we are asked for the elements with high relative frequency in a range, we can quickly return a short list of candidates that includes them. More specifically, given an m × n array A and a fraction α > 0, we can store A in O(mn(H + 1)log2(1 α) bits, where H is the entropy of the elements' distribution in A, such that later, given a rectangular range in A and a fraction β ≥ α, in O(1 β) time we can return a list of O(1 β) distinct array elements that includes all the elements that have relative frequency at least β in that range. We do not verify that the elements in the list have relative frequency at least β, so the list may contain false positives. In the case when m = 1, i.e., A is a string, we improve this space bound by a factor of log(1 α), and explore a spacetime trade off for verifying the frequency of the elements in the list. This leads to an O(n min(log(1 α), H +1) log n) bit data structure for strings that, in O(1 β) time, can return the O(1 β) elements that have relative frequency at least β in a given range, without false positives, for β ≥ α. In this paper, a new image set, called the Surrey Object Image Library (SOIL-47) is introduced, on which the performance of two colour-based object recognition methods is evaluated. The data was collected specifically for testing colour-based recognition algorithm and is publicly available. In the conducted experiments on SOIL-47, we evaluate two recognition algoritms; the Multimodal Neighbourhood Signature (MNS) approach and a method based on a Attributed Relational Graph (ARG). The MNS approach represents object appearance by measurements computed from image neighbourhoods with a multimodal colour density function. The ARG approach computes a graph of affine invariant measurements of the colour and shape of segmented image regions. Using only a single model image of each of the 47 objects, MNS performed well even for extreme test views close to degrees. The ARG method assumes a locally planar surface, therefore a second experiment was conducted on a subset of box-like objects of SOIL-47. MNS performance was fairly stable, outperforming ARG for most viewing angles.. Note, that this is the fir st systematic test of MNS with controlled 3D viewpoint change. Security-critical products rely on the secrecy and integrity of their cryptographic keys. This is challenging for low-cost resource-constrained embedded devices, with an attacker having physical access to the integrated circuit (IC). Physically, unclonable functions are an emerging technology in this market. They extract bits from unavoidable IC manufacturing variations, remarkably analogous to unique human fingerprints. However, post-processing by helper data algorithms (HDAs) is indispensable to meet the stringent key requirements: reproducibility, high-entropy, and control. The novelty of this paper is threefold. We are the first to provide an in-depth and comprehensive literature overview on HDAs. Second, our analysis does expose new threats regarding helper data leakage and manipulation. Third, we identify several hiatuses open problems in existing literature.