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Ahmed Helmy

Publications and source records attributed to Ahmed Helmy.

At least 37 records · Page 2Linked to original sources

Understanding periodicity and regularity of nodal encounters in mobile networks: A spectral analysis

Study on human mobility is gaining increasing attention from the research community with its multiple applications to use in mobile networks, particularly for the purpose of message delivery in the Delay Tolerant Networks. To better understand the potential of mobile nodes as message relays, our study investigates the encounter pattern of mobile devices. Specifically, we examine the extensive network traces that reflect mobility of communication devices. We analyze the periodicity in encounter pattern by using power spectral analysis. Strong periodicity was observed among rarely encountering mobile nodes while the periodicity was weaker among frequently encountering nodes. Further, we present a method to search regularly encountering pairs and discuss the findings. To our knowledge, we are the first to analyze the periodicity of encounter pattern with large network traces, which is a critical basis for designing an efficient delivery scheme using mobile nodes.

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Analysis of Spatio-Temporal Preferences and Encounter Statistics for DTN Performance

Spatio-temporal preferences and encounter statistics provide realistic measures to understand mobile user's behavioral preferences and transfer opportunities in Delay Tolerant Networks (DTNs). The time dependent behavior and periodic reappearances at specific locations can approximate future online presence while encounter statistics can aid to forward the routing decisions. It is theoretically shown that such characteristics heavily affect the performance of routing protocols. Therefore, mobility models demonstrating such characteristics are also expected to show identical routing performance. However, we argue models despite capturing these properties deviate from their expected routing performance. We use realistic traces to validate this observation on two mobility models. Our empirical results for epidemic routing show those models' largely differ (delay 67% & reachability 79%) from the observed values. This in-turn call for two important activities: (i) Analogous to routing, explore structural properties on a Global scale (ii) Design new mobility models that capture them.

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Similarity Analysis and Modeling in Mobile Societies: The Missing Link

A new generation of "behavior-aware" delay tolerant networks is emerging in what may define future mobile social networks. With the introduction of novel behavior-aware protocols, services and architectures, there is a pressing need to understand and realistically model mobile users behavioral characteristics, their similarity and clustering. Such models are essential for the analysis, performance evaluation, and simulation of future DTNs. This paper addresses issues related to mobile user similarity, its definition, analysis and modeling. To define similarity, we adopt a behavioral-profile based on users location preferences using their on-line association matrix and its SVD, then calculate the behavioral distance to capture user similarity. This measures the difference of the major spatio-temporal behavioral trends and can be used to cluster users into similarity groups or communities. We then analyze and contrast similarity distributions of mobile user populations in two settings: (i) based on real measurements from four major campuses with over ten thousand users for a month, and (ii) based on existing mobility models, including random direction and time-varying community models. Our results show a rich set of similar communities in real mobile societies with distinct behavioral clusters of users. This is true for all the traces studied, with the trend being consistent over time. Surprisingly, however, we find that the existing mobility models do not explicitly capture similarity and result in homogeneous users that are all similar to each other. Thus the richness and diversity of user behavioral patterns is not captured to any degree in the existing models. These findings strongly suggest that similarity should be explicitly captured in future mobility models, which motivates the need to re-visit mobility modeling in the future.

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Data-driven Co-clustering Model of Internet Usage in Large Mobile Societies

Design and simulation of future mobile networks will center around human interests and behavior. We propose a design paradigm for mobile networks driven by realistic models of users' on-line behavior, based on mining of billions of wireless-LAN records. We introduce a systematic method for large-scale multi-dimensional coclustering of web activity for thousands of mobile users at 79 locations. We find surprisingly that users can be consistently modeled using ten clusters with disjoint profiles. Access patterns from multiple locations show differential user behavior. This is the first study to obtain such detailed results for mobile Internet usage.

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PROTECT: Proximity-based Trust-advisor using Encounters for Mobile Societies

Many interactions between network users rely on trust, which is becoming particularly important given the security breaches in the Internet today. These problems are further exacerbated by the dynamics in wireless mobile networks. In this paper we address the issue of trust advisory and establishment in mobile networks, with application to ad hoc networks, including DTNs. We utilize encounters in mobile societies in novel ways, noticing that mobility provides opportunities to build proximity, location and similarity based trust. Four new trust advisor filters are introduced - including encounter frequency, duration, behavior vectors and behavior matrices - and evaluated over an extensive set of real-world traces collected from a major university. Two sets of statistical analyses are performed; the first examines the underlying encounter relationships in mobile societies, and the second evaluates DTN routing in mobile peer-to-peer networks using trust and selfishness models. We find that for the analyzed trace, trust filters are stable in terms of growth with time (3 filters have close to 90% overlap of users over a period of 9 weeks) and the results produced by different filters are noticeably different. In our analysis for trust and selfishness model, our trust filters largely undo the effect of selfishness on the unreachability in a network. Thus improving the connectivity in a network with selfish nodes. We hope that our initial promising results open the door for further research on proximity-based trust.

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SHIELD: Social sensing and Help In Emergency using mobiLe Devices

School and College campuses face a perceived threat of violent crimes and require a realistic plan against unpredictable emergencies and disasters. Existing emergency systems (e.g., 911, campus-wide alerts) are quite useful, but provide delayed response (often tens of minutes) and do not utilize proximity or locality. There is a need to augment such systems with proximity-based systems for more immediate response to attempt to prevent and deter crime. In this paper we propose SHIELD, an on-campus emergency rescue and alert management service. It is a fully distributed infrastructure-less platform based on proximity-enabled trust and cooperation. It relies on localized responses, sent using Bluetooth and/or WiFi on the fly to achieve minimal response time and maximal availability thereby augmenting the traditional notion of emergency services. Analysis of campus crime statistics and WLAN traces surprisingly show a strong positive correlation (over 55%) between on-campus crime statistics and spatio-temporal density distribution of on-campus mobile users. This result provides a motivation to develop such platform and points to the promise in reducing crime incidences. We also show an implementation of a prototype application to be used in such scenarios.

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Modeling Spatial and Temporal Dependencies of User Mobility in Wireless Mobile Networks

Realistic mobility models are fundamental to evaluate the performance of protocols in mobile ad hoc networks. Unfortunately, there are no mobility models that capture the non-homogeneous behaviors in both space and time commonly found in reality, while at the same time being easy to use and analyze. Motivated by this, we propose a time-variant community mobility model, referred to as the TVC model, which realistically captures spatial and temporal correlations. We devise the communities that lead to skewed location visiting preferences, and time periods that allow us to model time dependent behaviors and periodic re-appearances of nodes at specific locations. To demonstrate the power and flexibility of the TVC model, we use it to generate synthetic traces that match the characteristics of a number of qualitatively different mobility traces, including wireless LAN traces, vehicular mobility traces, and human encounter traces. More importantly, we show that, despite the high level of realism achieved, our TVC model is still theoretically tractable. To establish this, we derive a number of important quantities related to protocol performance, such as the average node degree, the hitting time, and the meeting time, and provide examples of how to utilize this theory to guide design decisions in routing protocols.

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CSI: A Paradigm for Behavior-oriented Delivery Services in Mobile Human Networks

We propose behavior-oriented services as a new paradigm of communication in mobile human networks. Our study is motivated by the tight user-network coupling in future mobile societies. In such a paradigm, messages are sent to inferred behavioral profiles, instead of explicit IDs. Our paper provides a systematic framework in providing such services. First, user behavioral profiles are constructed based on traces collected from two large wireless networks, and their spatio-temporal stability is analyzed. The implicit relationship discovered between mobile users could be utilized to provide a service for message delivery and discovery in various network environments. As an example application, we provide a detailed design of such a service in challenged opportunistic network architecture, named CSI. We provide a fully distributed solution using behavioral profile space gradients and small world structures. Our analysis shows that user behavioral profiles are surprisingly stable, i.e., the similarity of the behavioral profile of a user to its future behavioral profile is above 0.8 for two days and 0.75 for one week, and remains above 0.6 for five weeks. The correlation coefficient of the similarity metrics between a user pair at different time instants is above 0.7 for four days, 0.62 for a week, and remains above 0.5 for two weeks. Leveraging such a stability in user behaviors, the CSI service achieves delivery rate very close to the delay-optimal strategy (above 94%), with minimal overhead (less than 84% of the optimal). We believe that this new paradigm will act as an enabler of multiple new services in mobile societies, and is potentially applicable in server-based, heterogeneous or infrastructure-less wireless environments.

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Mining Behavioral Groups in Large Wireless LANs

One vision of future wireless networks is that they will be deeply integrated and embedded in our lives and will involve the use of personalized mobile devices. User behavior in such networks is bound to affect the network performance. It is imperative to study and characterize the fundamental structure of wireless user behavior in order to model, manage, leverage and design efficient mobile networks. It is also important to make such study as realistic as possible, based on extensive measurements collected from existing deployed wireless networks. In this study, using our systematic TRACE approach, we analyze wireless users' behavioral patterns by extensively mining wireless network logs from two major university campuses. We represent the data using location preference vectors, and utilize unsupervised learning (clustering) to classify trends in user behavior using novel similarity metrics. Matrix decomposition techniques are used to identify (and differentiate between) major patterns. While our findings validate intuitive repetitive behavioral trends and user grouping, it is surprising to find the qualitative commonalities of user behaviors from the two universities. We discover multi-modal user behavior for more than 60% of the users, and there are hundreds of distinct groups with unique behavioral patterns in both campuses. The sizes of the major groups follow a power-law distribution. Our methods and findings provide an essential step towards network management and behavior-aware network protocols and applications, to name a few.

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Encounter-based worms: Analysis and Defense

Encounter-based network is a frequently-disconnected wireless ad-hoc network requiring immediate neighbors to store and forward aggregated data for information disseminations. Using traditional approaches such as gateways or firewalls for deterring worm propagation in encounter-based networks is inappropriate. We propose the worm interaction approach that relies upon automated beneficial worm generation aiming to alleviate problems of worm propagations in such networks. To understand the dynamic of worm interactions and its performance, we mathematically model worm interactions based on major worm interaction factors including worm interaction types, network characteristics, and node characteristics using ordinary differential equations and analyze their effects on our proposed metrics. We validate our proposed model using extensive synthetic and trace-driven simulations. We find that, all worm interaction factors significantly affect the pattern of worm propagations. For example, immunization linearly decreases the infection of susceptible nodes while on-off behavior only impacts the duration of infection. Using realistic mobile network measurements, we find that encounters are bursty, multi-group and non-uniform. The trends from the trace-driven simulations are consistent with the model, in general. Immunization and timely deployment seem to be the most effective to counter the worm attacks in such scenarios while cooperation may help in a specific case. These findings provide insight that we hope would aid to develop counter-worm protocols in future encounter-based networks.

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On the Performance Evaluation of Encounter-based Worm Interactions Based on Node Characteristics

An encounter-based network is a frequently disconnected wireless ad-hoc network requiring nearby neighbors to store and forward data utilizing mobility and encounters over time. Using traditional approaches such as gateways or firewalls for deterring worm propagation in encounter-based networks is inappropriate. Because this type of network is highly dynamic and has no specific boundary, a distributed counter-worm mechanism is needed. We propose models for the worm interaction approach that relies upon automated beneficial worm generation to alleviate problems of worm propagation in such networks. We study and analyze the impact of key mobile node characteristics including node cooperation, immunization, on-off behavior on the worm propagations and interactions. We validate our proposed model using extensive simulations. We also find that, in addition to immunization, cooperation can reduce the level of worm infection. Furthermore, on-off behavior linearly impacts only timing aspect but not the overall infection. Using realistic mobile network measurements, we find that encounters are non-uniform, the trends are consistent with the model but the magnitudes are drastically different. Immunization seems to be the most effective in such scenarios. These findings provide insight that we hope would aid to develop counter-worm protocols in future encounter-based networks.

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IMPACT: Investigation of Mobile-user Patterns Across University Campuses using WLAN Trace Analysis

We conduct the most comprehensive study of WLAN traces to date. Measurements collected from four major university campuses are analyzed with the aim of developing fundamental understanding of realistic user behavior in wireless networks. Both individual user and inter-node (group) behaviors are investigated and two classes of metrics are devised to capture the underlying structure of such behaviors. For individual user behavior we observe distinct patterns in which most users are 'on' for a small fraction of the time, the number of access points visited is very small and the overall on-line user mobility is quite low. We clearly identify categories of heavy and light users. In general, users exhibit high degree of similarity over days and weeks. For group behavior, we define metrics for encounter patterns and friendship. Surprisingly, we find that a user, on average, encounters less than 6% of the network user population within a month, and that encounter and friendship relations are highly asymmetric. We establish that number of encounters follows a biPareto distribution, while friendship indexes follow an exponential distribution. We capture the encounter graph using a small world model, the characteristics of which reach steady state after only one day. We hope for our study to have a great impact on realistic modeling of network usage and mobility patterns in wireless networks.

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ATTENTION: ATTackEr traceback using MAC layer abNormality detecTION

Denial-of-Service (DoS) and Distributed DoS (DDoS) attacks can cause serious problems in wireless networks due to limited network and host resources. Attacker traceback is a promising solution to take a proper countermeasure near the attack origins, to discourage attackers from launching attacks, and for forensics. However, attacker traceback in Mobile Ad-hoc Networks (MANETs) is a challenging problem due to the dynamic topology, and limited network resources. It is especially difficult to trace back attacker(s) when they are moving to avoid traceback. In this paper, we introduce the ATTENTION protocol framework, which pays special attention to MAC layer abnormal activity under attack. ATTENTION consists of three classes, namely, coarse-grained traceback, fine-grained traceback and spatio-temporal fusion architecture. For energy-efficient attacker searching in MANETs, we also utilize small-world model. Our simulation analysis shows 79% of success rate in DoS attacker traceback with coarse-grained attack signature. In addition, with fine-grained attack signature, it shows 97% of success rate in DoS attacker traceback and 83% of success rate in DDoS attacker traceback. We also show that ATTENTION has robustness against node collusion and mobility.

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Efficient and Robust Geocasting Protocols for Sensor Networks

Geocasting is the delivery of packets to nodes within a certain geographic area. For many applications in wireless ad hoc and sensor networks, geocasting is an important and frequent communication service. The challenging problem in geocasting is distributing the packets to all the nodes within the geocast region with high probability but with low overhead. According to our study we notice a clear tradeoff between the proportion of nodes in the geocast region that receive the packet and the overhead incurred by the geocast packet especially at low densities and irregular distributions. We present two novel protocols for geocasting that achieve high delivery rate and low overhead by utilizing the local location information of nodes to combine geographic routing mechanisms with region flooding. We show that the first protocol Geographic-Forwarding-Geocast (GFG) has close-to-minimum overhead in dense networks and that the second protocol Geographic-Forwarding-Perimeter-Geocast (GFPG) provides guaranteed delivery without global flooding or global network information even at low densities and with the existence of region gaps or obstacles. An adaptive version of the second protocol (GFPG*) has the desirable property of perfect delivery at all densities and close-to-minimum overhead at high densities. We evaluate our mechanisms and compare them using simulation to other proposed geocasting mechanisms. The results show the significant improvement in delivery rate (up to 63% higher delivery percentage in low density networks) and reduction in overhead (up to 80% reduction) achieved by our mechanisms. We hope for our protocols to become building block mechanisms for dependable sensor network architectures that require robust efficient geocast services.

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Rendezvous Regions: A Scalable Architecture for Resource Discovery and Service Location in Large-Scale Mobile Networks

In large-scale wireless networks such as mobile ad hoc and sensor networks, efficient and robust service discovery and data-access mechanisms are both essential and challenging. Rendezvous-based mechanisms provide a valuable solution for provisioning a wide range of services. In this paper, we describe Rendezvous Regions (RRs) - a novel scalable rendezvous-based architecture for wireless networks. RR is a general architecture proposed for service location and bootstrapping in ad hoc networks, in addition to data-centric storage, configuration, and task assignment in sensor networks. In RR the network topology is divided into geographical regions, where each region is responsible for a set of keys representing the services or data of interest. Each key is mapped to a region based on a hash-table-like mapping scheme. A few elected nodes inside each region are responsible for maintaining the mapped information. The service or data provider stores the information in the corresponding region and the seekers retrieve it from there. We run extensive detailed simulations, and high-level simulations and analysis, to investigate the design space, and study the architecture in various environments including node mobility and failures. We evaluate it against other approaches to identify its merits and limitations. The results show high success rate and low overhead even with dynamics. RR scales to large number of nodes and is highly robust and efficient to node failures. It is also robust to node mobility and location inaccuracy with a significant advantage over point-based rendezvous mechanisms.

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Contact-Based Architecture for Resource Discovery (CARD) in Large Scale MANets

In this paper we propose a novel architecture, CARD, for resource discovery in large scale Mobile Ad hoc Networks (MANets) which, may scale up to thousands of nodes and may span wide geographical regions. Unlike previously proposed schemes, our architecture avoids expensive mechanisms such as global flooding as well as complex coordination between nodes to form a hierarchy. CARD is also independent of any external source of information such as GPS. In our architecture nodes within a limited number of hops from each node form the neighborhood of that node. Resources within the neighborhood can be readily accessed with the help of a proactive scheme within the neighborhood. For accessing resources beyond the neighborhood, each node also maintains a few distant nodes called contacts. Contacts help in creating a small world in the network and provide an efficient way to query for resources beyond the neighborhood. As the number of contacts of a node increases, the network view (reachability) of the node increases. Paths to contacts are validated periodically to adapt to mobility. We present mechanisms for contact selection and maintenance that attempt to increase reachability while minimizing overhead. Our simulation results show a clear trade-off between increase in reachability on one hand, and contact selection and maintenance overhead on the other. Our results suggest that CARD can be configured to provide a desirable reachability distribution for different network sizes. Comparisons with other schemes for resource discovery, such as flooding and bordercasting, show our architecture to be much more efficient and scalable.

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Efficient Micro-Mobility using Intra-domain Multicast-based Mechanisms (M&M)

One of the most important metrics in the design of IP mobility protocols is the handover performance. The current Mobile IP (MIP) standard has been shown to exhibit poor handover performance. Most other work attempts to modify MIP to slightly improve its efficiency, while others propose complex techniques to replace MIP. Rather than taking these approaches, we instead propose a new architecture for providing efficient and smooth handover, while being able to co-exist and inter-operate with other technologies. Specifically, we propose an intra-domain multicast-based mobility architecture, where a visiting mobile is assigned a multicast address to use while moving within a domain. Efficient handover is achieved using standard multicast join/prune mechanisms. Two approaches are proposed and contrasted. The first introduces the concept proxy-based mobility, while the other uses algorithmic mapping to obtain the multicast address of visiting mobiles. We show that the algorithmic mapping approach has several advantages over the proxy approach, and provide mechanisms to support it. Network simulation (using NS-2) is used to evaluate our scheme and compare it to other routing-based micro-mobility schemes - CIP and HAWAII. The proactive handover results show that both M&M and CIP shows low handoff delay and packet reordering depth as compared to HAWAII. The reason for M&M's comparable performance with CIP is that both use bi-cast in proactive handover. The M&M, however, handles multiple border routers in a domain, where CIP fails. We also provide a handover algorithm leveraging the proactive path setup capability of M&M, which is expected to outperform CIP in case of reactive handover.

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The STRESS Method for Boundary-point Performance Analysis of End-to-end Multicast Timer-Suppression Mechanisms

Evaluation of Internet protocols usually uses random scenarios or scenarios based on designers' intuition. Such approach may be useful for average-case analysis but does not cover boundary-point (worst or best-case) scenarios. To synthesize boundary-point scenarios a more systematic approach is needed.In this paper, we present a method for automatic synthesis of worst and best case scenarios for protocol boundary-point evaluation. Our method uses a fault-oriented test generation (FOTG) algorithm for searching the protocol and system state space to synthesize these scenarios. The algorithm is based on a global finite state machine (FSM) model. We extend the algorithm with timing semantics to handle end-to-end delays and address performance criteria. We introduce the notion of a virtual LAN to represent delays of the underlying multicast distribution tree. The algorithms used in our method utilize implicit backward search using branch and bound techniques and start from given target events. This aims to reduce the search complexity drastically. As a case study, we use our method to evaluate variants of the timer suppression mechanism, used in various multicast protocols, with respect to two performance criteria: overhead of response messages and response time. Simulation results for reliable multicast protocols show that our method provides a scalable way for synthesizing worst-case scenarios automatically. Results obtained using stress scenarios differ dramatically from those obtained through average-case analyses. We hope for our method to serve as a model for applying systematic scenario generation to other multicast protocols.

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