We study TDMA-scheduled Software Defined Wireless Multihop Networks (SDWMNs), whereby the data traffic and SDN control messages share the same network links and TDMA resources. Since the topology of WMNs dynamically changes, maintaining a responsive SDN plane is essential for meeting data traffic rate requirements. Placing more SDN controllers reduces communication delays at the SDN layer and increases its responsiveness. However, it demands more TDMA resources and reduces the available ones for data traffic. We analyze this trade-off between data traffic performance and SDN layer responsiveness by delving into two distinct resource allocation mechanisms in the WMN, the SDN controller placement and TDMA scheduling. We capture their interaction into an optimization problem formulation, which aims at maximizing the SDN-responsiveness subject to data traffic rate requirements, topology conditions, and the available TDMA resources. We propose a novel heuristic for the hard-to-solve problem that leverages the network state information gathered at the SDN layer. We find that our heuristic can increase the SDN-responsiveness by 44% when varying the rate reserved for rate-elastic data traffic within 40% of what is nominally requested. The heuristic is modular in accommodating different controller placement algorithms and robust to different alternative for the SDN software implementation.

In this paper, we iterate on Software Defined wireless Multihop Networks (SDWMNs) and TDMA-scheduled links, where data and SDN control traffic compete for the same resources. Two control functions are key in ensuring adequate Quality of Service (QoS) for data flows and high responsiveness (low latencies) for the SDN control-plane messages exchanged between the network nodes: the SDN Controller placement, which determines the paths of SDN control messages across the network and their overlap with the data traffic paths; and the TDMA scheduling, which distributes time slots between these two types of traffic, prioritizing them in different ways. Our take, in this paper, is that by coordinating these two control functions, rather than executing them independently, we can deal more efficiently with the data QoS-SDN responsiveness trade-off. We, thus, pursue the joint optimization of controller placement and link scheduling, formulating the respective optimization problem and proposing a novel heuristic algorithm for it. Its main idea is to, first, determine maximal sets of simultaneously transmitting non-interfering links to serve the data traffic requirements and, then, seek a Controller placement that takes best advantage of the spare link transmission opportunities in those sets. We compare our algorithm with benchmark solutions that carry out the two control functions independently and find that it trades far better the rate that can be allocated to data traffic with the communication delays at the SDN control plane.

Our work concerns online parking reservation platforms proposed in the last decade to cope with the parking challenge in cities worldwide. Enlisting parking resources from commercial operators (e.g., lots) and individuals (e.g., doorways) and letting drivers make online reservations through mobile apps, those platforms seek to ease transactions between the two sides and best match parking supply with parking demand. This way they maximize their value for drivers and parking space providers but also their revenue out of charged commissions. We distinguish between two types of commissions these platforms typically charge, fixed per transaction and proportional to its value, and formulate the respective optimization problems for the platform revenue maximization. We show that the two problems are NP-hard and design a novel algorithm that can treat both by combining greedy and dynamic programming principles. We study its optimality properties both analytically and experimentally, showing that the algorithm closely tracks optimal solutions for small and moderate problem sizes at run times that are several orders of size smaller than those needed by off-the-shelf ILP solvers. We then analyze real parking data we collected for the period 2018-2020 from the Bournemouth city in UK to realistically model the rich spatiotemporal dynamics of parking demand such as the location, start times and duration of parking events. These datasets drive the experimental evaluation of the proposed algorithm, which reports gains of up to 35% compared to the de facto parking resource reservation policy in such platforms. Notably, the highest gains are achieved when the platform operates under constrained supply conditions.

The demand for mobile data has been steadily increasing over the last decade, forming an ever-increasing portion of the overall Internet traffic. A great portion of this demand is still served through capped cellular data plans that charge a fixed fee for data consumption up to a cap and impose a typically higher penalty rate for consumption beyond that cap. It has been shown that when capped plans are shared, their caps are better utilized and the incurred penalty costs are amortized. This translates to subscription cost savings for the mobile users and better use of the cellular network resources for the mobile network operators. However, this sharing is nowadays restricted to closed groups (e.g., family members) or to multiple devices of a single user. In this paper we explore the generalization of capped data plan sharing to open user groups. We take the viewpoint of a platform that seeks to organize cellular users into subscription groups and recommend to them shared data plans on offer by mobile network operators that maximize their subscription cost savings. We first introduce a new cost-sharing rule, called double proportional cost sharing (DPCS), for splitting the subscription charges of the shared capped data plans “fairly” between subscription group members. We then formulate the two platform tasks into a joint optimization problem, characterize its complexity and devise three algorithms that leverage clustering techniques to solve it. Under ideal prediction of users’ data consumption all three algorithms achieve subscription savings beyond 50% for at least 70% of users and smaller but still significant savings for the rest of them, which are independent of the number of subscribers in all scenarios of practical interest. Notably, the best of the three algorithms preserves those savings when there is up to 10% bias in predicting the users’ data consumption and when this consumption exhibits elasticity to the data cap.

In participatory mobile crowdsensing (MCS) users repeatedly makechoicesamong a finite set of alternatives, i.e., whether to contribute to a task or not and which task to contribute to. The platform coordinating the MCS campaigns oftenengineersthese choices by selecting MCS tasks to recommend to users and offering monetary or in-kind rewards to motivate their contributions to them. In this paper, we revisit the well-investigated question of how to optimize the contributions of mobile end users to MCS tasks. However, we depart from the bulk of related literature by explicitly accounting for thebounded rationalityevidenced in human decision making. Bounded rationality is a consequence of cognitive and other kinds of constraints, e.g., time pressure, and has been studied extensively in behavioral science. We first draw on work in the field of cognitive psychology to model the way boundedly rational users respond to MCS task offers asFast-and-Frugal-Trees (FFTs). With each MCS task modeled as a vector of feature values, the decision process in FFTs proceeds through sequentially parsing lexicographically ordered features, resulting in choices that are satisfying but not necessarily optimal. We then formulate, analyze and solve the novel optimization problems that emerge for both nonprofit and for-profit MCS platforms in this context. The evaluation of our optimization approach highlights significant gains in both platform revenue and quality of task contributions when compared to heuristic rules that do not account for the lexicographic structure in human decision making. We show how this modeling framework readily extends to platforms that present multiple task offers to the users. Finally, we discuss how these models can be trained, iterate on their assumptions, and point to their implications for applications beyond MCS, where end-users make choices through the mediation of mobile/online platforms.

Community networks (CNs) are initiatives led by communities of people, who collectively contribute time, effort and resources to their purpose. Over the last two decades, they have proven their capacity to provide affordable connectivity in areas not attracting the interest of commercial operators, but also strengthen local community bonds. Nowadays, the realization of ambitious broadband connectivity agendas, the desire to bring online another billion of people in developing countries, but also concerns about concentration in the telecom market, motivate a more integral role of CNs in the globa lnetworking infrastructure. Prerequisites for this role are funding models that ensure their sustainable operation. In our paper, we study collective subscriptions, a novel subscription model that can be used to fund the CN activities. With collective subscriptions, a fixed subscription fee is charged per CN node and is shared between all individuals or households subscribing to the node. Maximizing the revenue out of the collective subscriptions while respecting the requirements for community inclusion turns out to be a complex problem with a non-trivial objective function. Hence, we look closer in to particular scenarios of interest and devise both exact and approximate algorithmic solutions for them. The evaluation of the scheme against both real and synthetic data shows that it combines higher subscription revenue with higher community inclusion when compared to the default fixed price individual subscription scheme. On a practical note, our analysis helps the CN operator understandand optimize this funding tool for sustainably engaging the community into the CN activities. The scheme itself could find more general use as a subscription model for other shared community resources such as computational power and storage space.

Efficient congestion control is critical to the operation of MPTCP, the Multipath extension of TCP. Congestion control in such an environment primarily aims at enhancing the cumulative TCP throughput over the available paths, while preserving TCP-friendliness by fairly sharing the available bandwidth with single-path TCP flows in each path. While most existing multipath congestion control algorithms fulfill the TCP-friendliness objective in their steady state, their throughput convergence latency is high, rendering them ineffective for short-lived flows. We have proposed Normalized Multipath Congestion Control (NMCC), an MPTCP congestion control algorithm that achieves TCP-friendliness faster, by normalizing the growth of individual sub-flow throughput rather than the throughput itself. As NMCC can become unfriendly when it experiences sparse congestion events, in this paper we introduce the extended NMCC (e-NMCC) protocol that caters for TCP-friendliness upon both throughput growth and throughput reduction epochs. We analytically characterize e-NMCC in terms of TCP-friendliness and responsiveness and compare it with alternative algorithms. Finally, we assess the performance of e-NMCC through experimentation with the htsim simulator and a real Linux implementation. Our results confirm that e-NMCC accelerates throughput convergence, thus ensuring TCP-friendliness regardless of connection duration and underlying network conditions.

In mobile crowdsensing (MCS), users are repeatedly asked to make choices between a set of alternatives, i.e., whether to contribute to a task or not and which task to contribute to. The platform coordinating the MCS campaigns engineers these choices by selecting the tasks to present to each user and offering incentives to ensure user contributions and maximize the benefit from them. In this paper, we revisit the well-investigated question of how to optimize the contributions of crowds of mobile end users to MCS tasks. However, we depart from the bulk of related literature by explicitly accounting for the bounded rationality of human decision making. Bounded rationality is a consequence of cognitive and other kinds of constraints, (e.g., time pressure) and has been studied extensively in behavioral science.We model bounded rationality after two instances of lexicographic decision-making models that originate in the field of cognitive psychology: Fast-and-Frugal-Trees (FFTs) and Discrete Elimination by Aspects (DEBA). With each MCS task modeled as a vector of feature values, the decision process under both models proceeds through sequentially parsing lexicographically ordered features, resulting in choices that are satisfying, but not necessarily optimal. We study, in particular, scenarios where a single task or a pair of tasks are presented to MCS users together with reward offers that adhere to per-task budget constraints. We formulate the optimization problems that emerge for the MCS campaign organizers as instances of the Generalized Assignment Problem (GAP), an NP-hard problem for which approximate algorithms are available. Our evaluation suggests that our optimization approach exhibits significant gains when compared to heuristic rules that do not account for the lexicographic structure in human decision making.

Our paper analyzes the role that crowdsourced community network (CN) infrastructures could undertake in coping with the financing needs of ambitious broadband connectivity visions. Key to this role are open business models fostering synergies of CNs with commercial Internet Service Providers (SPs). In such synergies, the SPs make their pricing policies commensurate with the investment of the community in order to fuel the CN growth and generate a market for their services. At the same time, they compete with each other for customer shares in this market. We formulate the leader-follower game that emerges out of the strategic interactions of the actors and compute numerically its equilibrium states under a broad range of scenarios drawing on real data. In all cases, our results point to mutual profits for all actors, rendering such synergies win-win strategies.

Caching decisions typically seek to cache content that satisfies the maximum possible demand aggregated over all users. Recommendation systems, on the contrary, focus on individual users and recommend to them appealing content in order to elicit further content consumption. In our paper, we explore how these, phenomenally conflicting, objectives can be jointly addressed. First, we formulate an optimization problem for the joint caching and recommendation decisions, aiming to maximize the cache hit ratio under minimal controllable distortion of the inherent user content preferences by the issued recommendations. Then, we prove that the problem is NP-complete and that its objective function lacks those monotonicity and submodularity properties that would guarantee its approximability. Hence, we proceed to introduce a simpler heuristic algorithm that essentially serves as a form of lightweight control over recommendations so that they are both appealing to end-users and friendly to network resources. Finally, we draw on both analysis and simulations with real and synthetic datasets to evaluate the performance of the algorithm. We point out its fundamental properties, provide bounds for the achieved cache hit ratio, and study its sensitivity to its own as well as system-level parameters.

Caching decisions by default seek to maximize some notion of social welfare: the content to be cached is determined so that the maximum possible aggregate demand over all users served by the cache is satisfied. Recommendation systems, on the contrary, are oriented towards user individual preferences: the recommended content should be most appealing to the user so as to elicit further content consumption. In our paper we explore how these, phenomenically conflicting, objectives can be jointly addressed. To this end, we depart radically from current practice with recommender systems, and we approach them as network traffic engineering tools that can actively shape content demand towards optimizing user- and network-centric performance objectives. We formulate the resulting joint theoretical optimization problem of deciding on the cached content and the recommendations to each user so that the cache hit ratio is maximized subject to a maximum tolerable distortion that the recommendation should undergo. We conclude on its complexity, and we propose a practical algorithm for its solution. The algorithm is essentially a form of lightweight control over the user recommendations so that the recommended content is both appealing to the end user and more friendly to the caching system and the network resources.

The message replication strategy, namely the way message copies are generated and diffused (a.k.a. sprayed) in the network, is a fundamental, yet not thoroughly explored, component of all multi-copy message forwarding schemes in opportunistic networks. Whereas almost all related protocols rely to some extent on the assistance of intermediate nodes for relaying message copies towards their destination, the generation of new message copies may either involve them or be carried out exclusively by the message source. This paper first formulates and solves analytical models for the performance of the two most popular message spraying strategies under imperfect node cooperation and homogeneous exponentially distributed pairwise node inter-contact times. Numerical results suggest that as the node cooperation decreases source replication consistently outperforms binary replication, i.e., the optimal variant of intermediate node-assisted replication under nominal conditions of full node cooperation. The analytical conclusions are also verified for more realistic node mobility patterns through trace-driven experimentation. We then formulate the ideal selection of replication mode as a finite-horizon Continuous Time Markov Decision Process with restricted decision epochs and solve it for the optimal spraying policy. The optimal policy coincides with the binary-(source-) spraying in the presence of few (resp. many) misbehaving nodes. To better approximate it at intermediate levels of misbehavior intensity, we introduce and analyze a simple static spraying policy permitting source-destination space-time paths of up to three hops. Our work deepens the understanding of a core operation embedded in a broad range of multi-copy DTN forwarding protocols. At the same time, it advocates a more thorough approach to the design and evaluation of DTN forwarding that accounts for beyond-nominal conditions.

We study the optimal design of mobile crowdsensing campaigns in terms of the aggregate quality of contributions attracted for a set of tasks. The interaction of the campaign with users is realized through a mobile app interface that recommends tasks to users and offers them incentives. The main contribution is a novel perspective on the payment distribution problem faced by the crowdsensing campaign organizer in light of originally unknown individual user preferences. Contrary to common practice, we acknowledge that users exhibit high diversity in decision making because they assess differently attributes related to a task such as their proximity to the place of interest (PoI), the payment made for contributing data, or the task context/theme. We draw on logistic-regression techniques from machine learning to learn users' individual preferences from past data rather than hypothesizing about them. We then formulate non-linear (sigmoid) optimization problems to determine the tasks and incentives (payments) that should be optimally offered to each user. Our mechanism is validated against synthetic but also real data about the way users choose tasks, collected through an online questionnaire. It achieves very good approximations of the optimal solutions and substantially outperforms alternative preference-agnostic policies that do not exercise behavioral user profiling to target the provision of incentives.

We look into the realization of mobile crowdsensing campaigns that draw on the opportunistic networking paradigm, as practised in delay-tolerant networks but also in the emerging device-to-device communication mode in cellular networks. In particular, we ask how mobile users can be optimally selected in order to generate the required space-time paths across the network for collecting data from a set of fixed locations. The users hold different roles in these paths, from collecting data with their sensing-enabled devices to relaying them across the network and uploading them to data collection points with Internet connectivity. We first consider scenarios with deterministic node mobility and formulate the selection of users as a minimum-cost set cover problem with a submodular objective function. We then generalize to more realistic settings with uncertainty about the user mobility. A methodology is devised for translating the statistics of individual user mobility to statistics of spacetime path formation and feeding them to the set cover problem formulation. We describe practical greedy heuristics for the resulting NP-hard problems and compute their approximation ratios. Our experimentation with real mobility datasets (a) illustrates the multiple tradeoffs between the campaign cost and duration, the bound on the hopcount of space-time paths, and the number of collection points; and (b) provides evidence that in realistic problem instances the heuristics perform much better than what their pessimistic worst-case bounds suggest.

The search for parking space in busy urban districts is one of those routine human activities that can benefit from the widespread adoption of pervasive sensing and radio communication technologies. Proposed parking assistance solutions combine sensors, either as fixed infrastructure or onboard vehicles, wireless networking technologies and mobile social applications running over smartphones to collect, share and present to drivers real-time information about parking availability and demand. One question that arises is how does (and should) the driver actually use such information to take parking decisions, e.g., whether to search for on-street parking space or drive to a parking lot and, in the latter case, which one. The paper is, hence, a performance analysis study that seeks to capture the highly behavioral and heuristic dimension of drivers' decisions and its impact on the efficiency of the parking search process. To this end we model drivers as agents of bounded rationality and consider lexicographic heuristics, an instance of the fast and frugal heuristics developed in behavioral sciences such as psychology and biology, as the mechanisms for their decisions.

Our paper investigates normative abstractions for the way drivers pursue parking space and respond to pricing policies about public and private parking facilities. The drivers are viewed as strategic agents who make rational decisions while attempting to minimize the cost of the acquired parking spots. We propose auction-based systems for realizing centralized parking allocation schemes, whereby drivers bid for public parking space and a central authority coordinates the parking assignments and payments. These are compared against the conventional uncoordinated parking search practice under fixed parking service cost, formulated as a resource selection game instance. In line with intuition, the auctioning system increases the revenue of the public parking operator exploiting the drivers' differentiated interest in parking. Less intuitively, the auction-based mechanism does not necessarily induce higher cost for the drivers: by avoiding the uncoordinated search and thus, eliminating the cruising cost, it turns out to be a preferable option for both the operator and the drivers under various combinations of parking demand and pricing policies.

Micro-blogging services have recently been experiencing increasing success among Web users. Different to traditional online social applications, micro-blogs are lightweight, require small cognitive effort and help share real-time information about personal activities and interests. In this article, we explore scalable pushing protocols that are particularly suited for the delivery of this type of service in a mobile pervasive environment. Here, micro-blog updates are generated and carried by mobile (smart-phone type) devices and are exchanged through opportunistic encounters. We enhance primitive push mechanisms using social information concerning the interests of network nodes as well as the frequency of encounters with them. This information is collected and shared dynamically, as nodes initially encounter each other and exchange their preferences, and directs the forwarding of micro-blog updates across the network. Also incorporated is the spatiotemporal scope of the updates, which is only partially considered in current Internet services. We introduce several new protocol variants that differentiate the forwarding strategy towards interest-similar and frequently encountered nodes, as well as the amount of updates forwarded upon each encounter. In all cases, the proposed scheme outperforms the basic flooding dissemination mechanism in delivering high numbers of micro-blog updates to the nodes interested in them. Our extensive evaluation highlights how use can be made of different amounts of social information to trade performance with complexity and computational effort. However, hard performance bounds appear to be set by the level of coincidence between interest-similar node communities and meeting groups emerging due to the mobility patterns of the nodes.

The optimal placement of service facilities largely determines the capability of a data network to efficiently support its users' service demands. As centralized solutions over large-scale distributed environments are extremely expensive, inefficient or even infeasible, distributed approaches that rely on partial topology and demand information are the only credible approaches to the service placement problem, even at the expense of non-guaranteed optimality. In this paper, we propose a distributed service migration heuristic that iteratively solves instances of the 1-median problem pushing progressively the service to more cost-effective locations. Key to our algorithm is a traffic-aware centrality metric, called weighted conditional betweenness centrality (wCBC), that captures the ability of a node to act as service demand concentrator and is employed in both selecting the nodes and setting their weights for the 1-median problem instance. The assessment of our heuristic proceeds in two steps. First, assuming (ideal) knowledge of the invoked wCBC metric, we carry out a proof-of-concept study that demonstrates the effectiveness of the heuristic over synthetic and real-world topologies as well as its advantages against comparable local-search-like migration schemes. Next, we devise practical protocol implementations that approximate the heuristic using local measurements of transit traffic and preserve the excellent accuracy and fast convergence properties of the algorithm for different routing policies. Our solution applies to a broad range of networking scenarios, and is very relevant to the emerging trends for in-network storage and involvement of the end-user in the creation and distribution of lightweight (autonomic) service facilities.

This paper explores how the degree of similarity within a social group can dictate the behavior of the individual nodes, so as to best tradeoff the individual with the social benefit. More specifically, we investigate the impact of social similarity on the effectiveness of content placement and dissemination. We consider three schemes that represent well the spectrum of behavior-shaped content storage strategies: the selfish, the self-aware cooperative, and the optimally altruistic ones. Our study shows that when the social group is tight (high degree of similarity), the optimally altruistic behavior yields the best performance for both the entire group (by definition) and the individual nodes (contrary to typical expectations). When the group is made up of members with almost no similarity, altruism or cooperation cannot bring much benefit to either the group or the individuals and thus, selfish behavior emerges as the preferable choice due to its simplicity. Notably, from a theoretical point of view, our “similarity favors cooperation” argument is inline with sociological interpretations of human altruistic behavior. On a more practical note, the self-aware cooperative behavior could be adopted as an easy to implement distributed alternative to the optimally altruistic one; it has close to the optimal performance for tight social groups and the additional advantage of not allowing mistreatment of any node, i.e., its induced content retrieval cost is always smaller than the cost of the selfish strategy.

The paper proposes an innovative method for the performance analysis of opportunistic forwarding protocols over files logging mobile node encounters (contact traces). The method is modular and evolves in three steps. It first carries out contact filtering to isolate contacts that constitute message forwarding opportunities for givenmessage coordinates and forwarding rules. It then draws on graph expansion techniques to capture these forwarding contacts into sparse space-time graph constructs. Finally, it runs standard shortest path algorithms over these constructs and derives typical performance metrics such as message delivery delay and path hopcount. The method is flexible in that it can easily assess the protocol operation under various expressions of imperfect node cooperation. We describe it in detail, analyze its complexity, and evaluate it against discrete event simulations for three representative randomized forwarding schemes. The match with the simulation results is excellent and obtained with run times up to three orders of size smaller than the duration of the simulations, thus rendering our method a valuable tool for the performance analysis of opportunistic forwarding schemes.

Interference is an inherent characteristic of wireless (multihop) communications. Adding interference-awareness to important control functions, e.g., routing, could significantly enhance the overall network performance. Despite some initial efforts, it is not yet clearly understood how to best capture the effects of interference in routing protocol design. Most existing proposals aim at inferring its effect by actively probing the link. However, active probe measurements impose an overhead and may often misrepresent the link quality due to their interaction with other networking functions. Therefore, in this paper we follow a different approach and: 1) propose a simple yet accurate analytical model for the effect of interference on data reception probability, based only on passive measurements and information locally available at the node; 2) use this model to design an efficient interference-aware routing protocol that performs as well as probing-based protocols, yet avoids all pitfalls related to active probe measurements. To validate our proposal, we have performed experiments in a real testbed, setup in our indoor office environment. We show that the analytical predictions of our interference model exhibit good match with both experimental results as well as more complicated analytical models proposed in related literature. Furthermore, we demonstrate that a simple probeless routing protocol based on our model performs at least as good as well-known probe-based routing protocols in a large set of experiments including both intraflow and interflow interference.

This paper explores how the degree of similarity within a social group can be exploited in order to dictate the behavior of the individual nodes, so as to best accommodate the typically non-coinciding individual and social benefit maximization. More specifically, this paper investigates the impact of social similarity on the effectiveness of content dissemination, as implemented through three classes representing well the spectrum of behavior-shaped content storage strategies: the selfish, the self-aware cooperative and the optimally altruistic ones. This study shows that when the social group is tight (high degree of similarity), the optimally altruistic behavior yields the best performance for both the entire group (by definition) and the individual nodes (contrary to typical expectations). When the group is made up of foreigners with almost no similarity, altruism or cooperation cannot bring much benefits to either the group or the individuals and thus, a selfish behavior would make sense due to its simplicity. Finally, the self-aware cooperative behavior could be adopted as an easy to implement distributed scheme — compared to the optimally altruistic one — that has close to the optimal performance for tight social groups, and has the additional advantage of not allowing mistreatment to any node (i.e., the content retrieval cost become larger compared to the cost of the selfish strategy).

Mobile wireless network research focuses on scenarios at the extremes of the network connectivity continuum where the probability of all nodes being connected is either close to unity, assuming connected paths between all nodes (mobile ad hoc networks), or it is close to zero, assuming no multi-hop paths exist at all (delay-tolerant networks). In this paper, we argue that a sizable fraction of networks lies between these extremes and is characterized by the existence of partial paths, i.e., multi-hop path segments that allow forwarding data closer to the destination even when no end-to-end path is available. A fundamental issue in such networks is dealing with disruptions of end-to-end paths. Under a stochastic model, we compare the performance of the established end-to-end retransmission (ignoring partial paths), against a forwarding mechanism that leverages partial paths to forward data closer to the destination even during disruption periods. Perhaps surprisingly, the alternative mechanism is not necessarily superior. However, under a stochastic monotonicity condition between current vs. future path length, which we demonstrate to hold in typical network models, we manage to prove superiority of the alternative mechanism in stochastic dominance terms. We believe that this study could serve as a foundation to design more efficient data transfer protocols for partially-connected networks, which could potentially help reducing the gap between applications that can be supported over disconnected networks and those requiring full connectivity.

In this paper we focus on the provision of proportional class-based service differentiation to transmission control protocol (TCP) flows in the context of bandwidth on demand (BoD) split-TCP geostationary (GEO) satellite networks. Our approach involves the joint configuration of TCP-Performance Enhancing Proxy (TCP-PEP) agents at the transport layer and the scheduling algorithm controlling the resource allocation at the Medium Access Control (MAC) layer. We show that the two differentiation mechanisms exhibit complementary behavior in achieving the desired differentiation throughout the traffic load space: the TCP-PEPs control differentiation at low and medium system utilization, whereas the MAC scheduler becomes the dominant differentiation factor under high traffic load. The main challenge for the satellite operator is to appropriately configure those two mechanisms to achieve a specific differentiation target for the different classes of TCP flows. To this end, we propose a fixed-point framework to analytically approximate the achieved differentiated TCP performance. We validate the predictive capacity of our analytical method via simulations and show that our approximations closely match the performance of different classes of TCP flows under various scenarios for the network traffic load and configuration of the MAC scheduler and TCP-PEP agent. Satellite network operators could use our approximations as an analytical tool to tune their networks.

Interference is an inherent property of wireless multihop networks. Adding interference-awareness to their control functions can significantly enhance the overall network performance. In this paper we present an analytical model for the probability that a transmission destined to an arbitrary network node is successful in the presence of interference from other nodes in the network. We introduce the concept of interference areas and interference zones to express this probability as a function of the network density, node transmission probability, radio propagation environment, and network card reception sensitivity. Our derivation includes a simpleMAC model, which captures the carrier sense function of many MAC protocols. Contrary to measurementbased models, our derivation only requires information that is locally available to the nodes, avoiding all measurement-related pitfalls. The validation of our model against experiments in a real testbed, set up for this purpose in our indoor office environment, shows good match of the experimental results with the analytical predictions. Interestingly our model predictions follow closely those of more elaborate state-of-the-art analytical models. Finally, to demonstrate the real utility of our model, we have implemented on our testbed a routing metric that explicitly takes interference into account via our derivation. The throughputs of the resulting routes compare favorably with those achieved by a well-known probe-based routing metric.

Models of traffic demand are fundamental inputs to the design and engineering of data networks. In this paper we address this requirement in the context of large-scale wireless infrastructures using real measurement data from the University of North Carolina (UNC) wireless campus network. Our modeling effort focuses on capturing the demand variation in both the spatial and temporal domain in a way that scales well with the size of the wireless network. The network traffic dynamics are studied over two different week-long monitoring periods at various levels of spatial aggregation, from individual buildings to the whole network. We model traffic workload in terms of wireless sessions and network flows and find several modeling elements that are reusable in both temporal and spatial dimensions. The same set of parametric distributions for the session-and flow-related traffic variables capture the network traffic demand in both monitoring periods. Even more interestingly, these same distributions can characterize traffic dynamics at finer spatial scales, such as a single building or a group of buildings. We use our models to generate synthetic traffic and compare with trace data. The comparison clearly illustrates the trade-off between model scalability and reusability, on the one hand, and accuracy in capturing local-scale traffic dynamics on the other. Our main contribution is a novel behavioral approach for traffic demand modeling in large wireless networks that features high flexibility in the exploitation of the spatial and temporal resolution available in data traces.

Campus wireless LANs (WLANs) are complex systems with hundreds of access points (APs) and thousands of users. To analyze the performance of wireless networking protocols, researchers need to construct simulations and testbed experiments that reproduce the characteristics of these networks. However, the generation of realistic models and benchmarks is challenging and there is only a limited set of models of roaming and access based on real measurement data. We employed graph theory, modeled the roaming activity as a graph and measured its degree of connectivity. The negative binomial distribution models well the degree of connectivity. Furthermore, we analyzed the evolution of the roaming activity in the spatial and temporal domain and its impact on the degree of connectivity of the graph.

The rapid emergence of multimedia applications in the Internet has highlighted the need for service differentiation in broadband satellite networks, which aim at being an integral part of the broadband network infrastructure. This paper presents a novel scheduler, called SWTP (satellite waiting time priority), to provide relative service differentiation in a DVB-RCS geostationary (GEO) satellite system where the network is structured to support a finite number of ordered service classes. We advocate the adoption of the proportional service differentiation model in the satellite domain to provide proportional delay differentiation to different traffic classes. The lightweight nature of the model makes it especially suitable for satellite systems as it minimizes computational cost by doing away with mechanisms such as admission control and resource reservation. Simulation results suggest that the SWTP scheduler can effectively and consistently provide proportional delay differentiation in satellite networks.

The paper addresses TCP performance enhancing proxy techniques broadly deployed in wireless networks. Drawing on available models for TCP latency, we describe an analytical model for the latency and the buffer requirements related to the split-TCP mechanism. Although the model applicability is broad, we present and evaluate the model in the context of geostationary satellite networks, where buffering requirements may become more dramatic. Simulation results are compared with the analytical model estimates and show that the model captures the impact of various parameters affecting the dynamics of the component connections traversing the terrestrial and the satellite network.

We investigate the use of fixed-point methods for predicting the performance of multiple TCP flows sharing geostationary satellite links. The problem formulation is general in that it can address both error-free and error-prone links, proxy mechanisms such as split-TCP connections and account for asymmetry and different satellite network configurations. We apply the method in the specific context of bandwidth on demand (BoD) satellite links. The analytical approximations show good agreement with simulation results, although they tend to be optimistic when the link is not saturated. The main constraint upon the method applicability is the limited availability of analytical models for the MAC-induced packet delay under non-homogeneous load and prioritization mechanisms.

The elasticity of transmission control protocol (TCP) traffic complicates attempts to provide performance guarantees to TCP flows. The existence of different types of networks and environments on the connections' paths only aggravates this problem. In this paper, simulation is the primary means for investigating the specific problem in the context of bandwidth on demand (BoD) geostationary satellite networks. Proposed transport-layer options and mechanisms for TCP performance enhancement, studied in the single connection case or without taking into account the media access control (MAC)-shared nature of the satellite link, are evaluated within a BoD-aware satellite simulation environment. Available capabilities at MAC layer, enabling the provision of differentiated service to TCP flows, are demonstrated and the conditions under which they perform efficiently are investigated. The BoD scheduling algorithm and the policy regarding spare capacity distribution are two MAC-layer mechanisms that appear to be complementary in this context; the former is effective at high levels of traffic load, whereas the latter drives the differentiation at low traffic load. When coupled with transport layer mechanisms they can form distinct bearer services over the satellite network that increase the differentiation robustness against the TCP bias against connections with long round-trip times. We also explore the use of analytical, fixed-point methods to predict the performance at transport level and link level. The applicability of the approach is mainly limited by the lack of analytical models accounting for prioritization mechanisms at the MAC layer and the nonuniform distribution of traffic load among satellite terminals.

This paper reports a simulation study of the interaction between the transmission control protocol (TCP) and bandwidth on demand (BoD) mechanisms in broadband satellite networks. It forms part of a broader effort aiming at the identification of the proper mechanisms and algorithms to efficiently carry TCP traffic over these networks. We investigate two aspects of this rather complicated interaction, related to the possible BoD configurations and the TCP tuning parameters, focusing on GEO satellite networks. The impact on TCP performance and system efficiency is evaluated and effects that cannot be easily captured by analysis are highlighted.