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Ensuring that information flowing through a network is secure from manipulation and eavesdropping by unauthorized parties is an important task for network administrators. Many cyber attacks rely on a lack of network-level information flow controls to successfully compromise a victim network. Once an adversary exploits an initial entry point, they can eavesdrop and move laterally within the network (e.g., scan and penetrate internal nodes) to further their malicious goals. In this article, we propose a novel multilevel security (MLS) framework to enforce a secure inter-node information flow policy within the network and therein vastly reduce the attack surface available to an adversary who has penetrated it. In contrast to prior work on multilevel security in computer networks which relied on enforcing the policy at network endpoints, we leverage the centralization of software-defined networks (SDNs) by moving the task to the controller and providing this service transparently to all network nodes. Our framework, MLSNet , formalizes the generation of a policy compliant network configuration (i.e., set of flow rules on the SDN switches) as network optimization problems, with the objectives of (1) maximizing the number of flows satisfying all security constraints and (2) minimizing the security cost of routing any remaining flows to guarantee availability. We demonstrate that MLSNet can securely and efficiently route flows that satisfy the security constraints and route the remaining flows with a minimal security cost (e.g., route >85% of flows, where the heuristic achieves 89% and 87% of the optimal solutions for the optimization problems).

Eavesdroppers can exploit exposed packet headers towards attacks that profile clients and their data flows. In this paper, we propose FOG, a framework for effective full and partial header blinding using MIMO, to thwart eavesdroppers. FOG effectively tracks header bits as they traverse physical (PHY) layer sub-systems that perform functions like scrambling and interleaving. It combines multiple blinding signals for more effective and less predictable obfuscation, as compared to using a fixed blinding signal. We implement FOG on the WARP platform and demonstrate via extensive experiments that it yields better obfuscation than prior schemes that deploy full packet blinding. It causes a bit error rate (BER) of > 40 % at an eavesdropper if two blinding streams are sent during header transmissions. Furthermore, even with full header blinding, FOG incurs a very small throughput hit of $\approx ~5$ % with one blinding stream (and 9 % with two streams). Full packet blinding incurs much higher throughput hits (25 % with one stream and 50 % with two streams).

One of the main features of 5G networks is to provide high data rates with low variability to mobile users. However, this has been shown to be ineffective from the operator's perspective as it leads to abundantly unused network resources. Reallocating the unused resources to the same users increases back the data rate variability. A more efficient way is to provide different low-variability data rates to the users depending on their channel conditions, while not wasting network resources considerably. In this paper, the goal is to minimize the coefficient of variation across all users in the cell. To that end, we consider two approaches, one with reserved resources for every user and the other where the amount of allocated resources is decided on the fly. Then, for each of them, we look at different allocation policies and derive the maximum achievable constant rate for every user. We evaluate the performance on a real trace. Results show that utilizing policies with no-resource-reservation increases considerably the utilization of network resources and the achievable data rates at the expense of a slight increase in variability. Also, using our policies, the waste of resources drops to 15%, which is almost 4x lower than with other state-of-the-art techniques.

Ultra-Reliable Low-Latency Communications (URLLC) are one of the service types supported by 5G. These are characterized by a high reliability of delivering packets within a short deadline. To fulfill these stringent requirements, a special care must be taken to determine the required data rate of a user, given its traffic intensity and channel conditions. Furthermore, with the network resources being limited and the competition between the users in the cell, an important question that arises is that of admission control, so that the admitted users do not experience performance deterioration. In this paper, we provide the analysis that leads to an admission control policy. We do this for two types of users in terms of their traffic intensities and channel conditions: 1) homogeneous users, and 2) heterogeneous users. We validate our results on a trace. Results show that the number of admitted users depends on the traffic intensity and the worst channel conditions. An increase in traffic intensity by 3 times can decrease the number of admitted users by almost 35%.

Massive Machine-Type Communications (mMTC) are one of the service types supported by 5G. These are characterized by the need to serve a large number of devices that have only sporadic traffic and which have low-energy consumption. While the non-intensive nature of the traffic does not pose much strain on efficient network planning, the requirement to serve a large number of devices certainly does. With the network resources being limited and the competition between users in the cell, an important question that arises is that of admission control, so that the admitted users can be successfully served by the network. To this end, in this paper, we provide the analysis that leads to an admission control policy. We do this for two types of users in terms of their traffic intensities and channel conditions: 1) homogeneous users, and 2) heterogeneous users. We validate our results on a trace. Results show that the number of admitted users depends on the traffic pattern but not on the channel conditions. An increase in traffic intensity by 15 times can decrease the number of admitted users by 15$ times.

Video streaming on mobile devices necessitates a balance between a users' Quality of Experience (QoE) and energy consumption. Given large data sizes from video, extensive amounts of battery power are required for downloading, processing, and playing each video segment. However, video quality may suffer drastically in an effort to pursue energy efficiency. In balancing these two objectives, our research incorporates advanced energy models, processor clock rates, buffer management, and network quality aware downloading. Furthermore, multi-user systems present unique challenges in optimization given competition for network resources. Our algorithm accounts for these fluctuations and concurrent demands across both many users and many cells. Present application in LTE networks and foundations for future implementation in 5G systems are provided.

Advanced targeted cyber attacks often rely on reconnaissance missions to gather information about potential targets, their characteristics and location to identify vulnerabilities in a networked environment. Advanced network scanning techniques are often used for this purpose and are automatically executed by malware infected hosts. In this paper, we formally define network deception to defend reconnaissance and develop a reconnaissance deception system, which is based on software defined networking, to achieve deception by simulating virtual topologies. Our system thwarts network reconnaissance by delaying the scanning techniques of adversaries and invalidating their collected information, while limiting the performance impact on benign network traffic. By simulating the topological as well as physical characteristics of networks, we introduce a system which deceives malicious network discovery and reconnaissance techniques with virtual information, while limiting the information an attacker is able to harvest from the true underlying system. This approach shows a novel defense technique against adversarial reconnaissance missions which are required for targeted cyber attacks such as advanced persistent threats in highly connected environments. The defense steps of our system aim to invalidate an attackers information, delay the process of finding vulnerable hosts and identify the source of adversarial reconnaissance within a network.

Load balancers enable efficient use of network resources by distributing traffic fairly across them. In software-defined networking (SDN), load balancing is most often realized by a controller application that solicits traffic load reports from network switches and enforces load balancing decisions through flow rules. This separation between the control and data planes in SDNs creates an opportunity for an adversary at a compromised switch to misreport traffic loads to influence load balancing. In this paper, we evaluate the ability of such an adversary to control the volume of traffic flowing through a compromised switch by misreporting traffic loads. We use a queuing theoretic approach to model the attack and develop algorithms for misreporting that allow an adversary to tune attack parameters toward specific adversarial goals. We validate the algorithms with a virtual network testbed, finding that through misreporting the adversary can draw nearly all of the load in the subnetwork (+750%, or 85% of the load in the system), or an adversary-desired amount of load (a target load, e.g., +200%) to within 12% error of that target. This is yet another example of how depending on untrustworthy reporting in making control decisions can lead to fundamental security failures.

Densely-deployed femtocell networks are used to enhance wireless coverage in public spaces such as office buildings, subways, and academic buildings. These networks can increase user throughput, but edge users can suffer from co-channel interference and service outages. This paper introduces a distributed algorithm for network configuration, called Radius Reduction and Scheduling (RRS), to improve the performance and fairness of the network. RRS works by jointly adapting femtocell transmission power, allocating user to femtocells, and scheduling resource blocks so as to increase fairness and reduce outage probability in dense femtocell networks. RRS produces a network configuration that guarantees either user or area coverage depending on the management needs. A prototype implementation confirms the benefits of RRS in a real environment. Furthermore, extensive simulations show that RRS reduces the outage probability of up to 50%, and provides better fairness, with an increase in Jain’s index of 190%, with respect to a baseline algorithm which works with fixed power and best-effort scheduling and to a previous approach to resource management in femtocell networks.

Consistent rate provisioning is one of the most prominent features envisioned in next generation of cellular networks (5G). However, it has been shown that providing a consistent data rate to users leads to severe underutilization of the available resources, making the cellular operator function very inefficiently. A possible way to increase the efficiency is by reallocating the unused resources to the same users. In this paper, we quantify the benefits offered by different reallocation policies both for mobile operator and the users. First, we focus on increasing the efficiency (total throughput) of the cellular network operator and then on providing fairness to mobile users. In both cases, we determine the optimal policy. The theoretical analysis is followed by extensive realistic simulations, where we compare the performance of different reallocation policies with that of a benchmark model (fair share of the resources with no consistency), and show that the right reallocation policy improves the performance significantly.

When routing flows in a software defined network (SDN), service disruption and inconsistencies can occur during the updates of routing tables leading to degraded QoS or interruption of existing services. We study the problem of rerouting existing flows in an SDN to enable the admission of new flows while minimizing the disruption of existing flows, under link capacity and Quality of Service (QoS) constraints. We formulate the problem as an integer linear programming problem and propose two randomized rounding algorithms with bounded congestion and demand loss to solve this problem.

Providing a consistent data rate to users with a highly dynamic activity in a cellular network is a very important feature of 5G. This will lead to increased user satisfaction with services such as video-streaming, online gaming, etc. Knowing that resources are constrained, operators must limit the number of users in the cell, so that admitted users do not experience performance deterioration. In this paper, we consider the problem of admission control for consistent users in 5G cellular systems. We consider two types of users in terms of their rate distributions: 1) homogeneous users, and 2) heterogeneous users. For each user type, we perform the analysis for two scenarios: constant number of users, and random number of users. The analysis is followed by realistic simulations to show that our models can provide satisfactory results in realistic scenarios as well. We also show that the number of admitted users can be significantly increased (up to 3 ×) by allowing a slight deterioration in the QoS.

When multiple networks are interconnected because of mutual service interdependence, propagation of phenomena across the networks is likely to occur. Depending on the type of networks and phenomenon, the propagation may be a desired effect, such as the spread of information or consensus in a social network, or an unwanted one, such as the propagation of a virus or a cascade of failures in a communication or service network. In this paper, we propose a general analytic model that captures multiple types of dependency and of interaction among nodes of interdependent networks, that may cause the propagation of phenomena. The above model is used to evaluate the effects of different diffusion models in a wide range of network topologies, including different models of random graphs and real networks. We propose a new centrality metric and compare it to more traditional approaches to assess the impact of individual network nodes in the propagation. We propose guidelines to design networks in which the diffusion is either a desired phenomenon or an unwanted one, and consequently must be fostered or prevented, respectively. We performed extensive simulations to extend our study to large networks and to show the benefits of the proposed design solutions.

Over-provisioned femtocell networks can be used to serve indoor locations that see high peak loads, such as airports or train stations. However, networks designed for high peak loads are mostly under-utilized, which is wasteful from an energy-use perspective. This paper introduces a femtocell selective activation problem. We motivate the use of selective activation in femtocell networks using real femtocell power measurements. We formally define the selective activation problem, and introduce GreenFemto, a distributed femtocell selective activation algorithm. We prove that GreenFemto converges to a locally Pareto optimal solution. Detailed simulations of an LTE wireless system are used to demonstrate the performance of GreenFemto. We find that GreenFemto uses up to 55% fewer femtocells to serve a given load, relative to an existing femtocell power-saving technique. Furthermore, we show that GreenFemto comes within 15% of a globally optimal solution. We conclude that selective activation can be successfully applied to femtocell networks to both reduce power consumption, and reduce outage probabilities.

Network coding has been shown to offer significant throughput benefits over certain wireless network topologies. However, the application of network coding may not always improve the network performance. In this paper, we first provide an analytical study, which helps in assessing when network coding is preferable to a traditional store-and-forward approach. Interestingly, our study reveals that in many topological scenarios, network coding can in fact hurt the throughput performance; in such scenarios, applying the store-and-forward approach leads to higher network throughput. We validate our analytical findings via extensive testbed experiments. Guided by our findings as our primary contribution, we design and implement PACE, a Policy-Aware Coding Enforcement logic that enables network coding only when it is expected to offer performance benefits. Specifically, PACE leverages a minimal set of periodic link quality measurements in order to make per-flow online decisions with regards to when network coding should be activated, and when store-and-forward is preferable. It can be easily embedded into network-coding-aware routers as a user-level or kernel-level software utility. We evaluate the efficacy of PACE via: 1) ns-3 simulations, and 2) experiments on a wireless testbed. We observe that our scheme wisely activates network coding only when appropriate, thereby improving the total network throughput by as much as 350% in some scenarios.

Mobile edge computing is an emerging technology to offer resource-intensive yet delay-sensitive applications from the edge of mobile networks, where a major challenge is to allocate limited edge resources to competing demands. While prior works often make a simplifying assumption that resources assigned to different users are non-sharable, this assumption does not hold for storage resources, where users interested in services (e.g., data analytics) based on the same set of data/code can share storage resource. Meanwhile, serving each user request also consumes non-sharable resources (e.g., CPU cycles, bandwidth). We study the optimal provisioning of edge services with non-trivial demands of both sharable (storage) and non-sharable (communication, computation) resources via joint service placement and request scheduling. In the homogeneous case, we show that while the problem is polynomial-time solvable without storage constraints, it is NP-hard even if each edge cloud has unlimited communication or computation resources. We further show that the hardness is caused by the service placement subproblem, while the request scheduling subproblem is polynomial-time solvable via maximum-flow algorithms. In the general case, both subproblems are NP-hard. We develop a constant-factor approximation algorithm for the homogeneous case and efficient heuristics for the general case. Our trace-driven simulations show that the proposed algorithms, especially the approximation algorithm, can achieve near-optimal performance, serving 2-3 times more requests than a baseline solution that optimizes service placement and request scheduling separately.

Eavesdroppers can exploit exposed packet headers towards attacks that profile clients and their data flows. In this paper, we propose FOG, a framework for effective header blinding using MIMO, to thwart eavesdroppers. FOG effectively tracks header bits as they traverse physical (PHY) layer sub-systems that perform functions like scrambling and interleaving. It combines multiple blinding signals for more effective and less predictable obfuscation, as compared to using a fixed blinding signal. We implement FOG on the WARP platform and demonstrate via extensive experiments that it yields better obfuscation than prior schemes that deploy full packet blinding. It causes a bit error rate (BER) of > 40 % at an eavesdropper if two blinding streams are sent during header transmissions. Furthermore, FOG incurs a very small throughput hit of ≈ 5 % with one blinding stream (and 9 % with two streams). Full packet blinding incurs much higher throughput hits (25 % with one stream and 50 % with two streams).

We present a framework for non-asymptotic analysis of real-world multi-hop wireless networks that captures protocol overhead, congestion bottlenecks, traffic heterogeneity and other real-world concerns. The framework introduces the concept of symptoticscalability to determine the number of nodes to which a network scales, and a metric called change impact value for comparing the impact of underlying system parameters on network scalability. A key idea is to divide analysis into generic and specific parts connected via a signature--a set of governing parameters of a network scenario--such that analyzing a new network scenario reduces mainly to identifying its signature. Using this framework, we present the first closed-form symptotic scalability expressions for line, grid, clique, randomized grid and mobile topologies. We model both TDMA and 802.11, as well as unicast and broadcast traffic. We compare the analysis with discrete event simulations and show that the model provides sufficiently accurate estimates of scalability. We show how our impact analysis methodology can be used to progressively tune network features to meet a scaling requirement. We uncover several new insights, for instance, on the limited impact of reducing routing overhead, the differential nature of flooding traffic, and the effect real-world mobility on scalability. Our work is applicable to the design and deployment of real-world multi-hop wireless networks including community mesh networks, military networks, disaster relief networks and sensor networks.

Community detection is an important issue due to its wide use in designing network protocols such as data forwarding in Delay Tolerant Networks (DTN) and worm containment in Online Social Networks (OSN). However, most of the existing community detection algorithms focus on binary networks. Since most networks are naturally weighted such as DTN or OSN, in this article, we address the problems of community detection in weighted networks, exploit community for data forwarding in DTN and worm containment in OSN, and demonstrate how community can facilitate these network designs. Specifically, we propose a novel community detection algorithm, and introduce two metrics: intra-centrality and inter-centrality, to characterize nodes in communities, based on which we propose an efficient data forwarding algorithm for DTN and a worm containment strategy for OSN. Extensive trace-driven simulation results show that the proposed community detection algorithm, the data forwarding algorithm, and the worm containment strategy significantly outperform existing works.

Traditional routing metrics designed for wireless networks are application-agnostic. In this paper, we consider a wireless network where the application flows consist of video traffic. From a user perspective, reducing the level of video distortion is critical. We ask the question "Should the routing policies change if the end-to-end video distortion is to be minimized?" Popular link-quality-based routing metrics (such as ETX) do not account for dependence (in terms of congestion) across the links of a path; as a result, they can cause video flows to converge onto a few paths and, thus, cause high video distortion. To account for the evolution of the video frame loss process, we construct an analytical framework to, first, understand and, second, assess the impact of the wireless network on video distortion. The framework allows us to formulate a routing policy for minimizing distortion, based on which we design a protocol for routing video traffic. We find via simulations and testbed experiments that our protocol is efficient in reducing video distortion and minimizing the user experience degradation.

Attacker VMs try to co-reside with victim VMs on the same physical infrastructure as a precursor to launching attacks that target information leakage. VM migration is an effective countermeasure against attempts at malicious co-residency. In this paper, we first undertake an experimental study on Amazon EC2 to obtain an in-depth understanding of the side-channels an attacker can use to ascertain co-residency with a victim. Here, we identify a new set of stealthy side-channel attacks which, we show to be more effective than currently available attacks towards verifying co-residency. Based on the study, we develop a set of guidelines to determine under what conditions victim VM migrations should be triggered given performance costs in terms of bandwidth and downtime, that a user is willing to bear. Via extensive experiments on our private in-house cloud, we show that migrations using our guidelines can limit the fraction of the time that an attacker VM co-resides with a victim VM to about 1 % of the time with bandwidth costs of a few MB and downtimes of a few seconds, per day per VM migrated.

Software Defined Networking (SDN), and its popular implementation OpenFlow, represent the foundation for the design and implementation of modern networks. The essential part of an SDN-based network are flow rules that enable network elements to steer and control the traffic and deploy policy enforcement points with a fine granularity at any entry-point in a network. Such applications, implemented with the usage of OpenFlow rules, are already integral components of widely used SDN controllers such as Floodlight or OpenDayLight. The implementation details of network policies are reflected in the composition of flow rules and leakage of such information provides adversaries with a significant attack advantage such as bypassing Access Control Lists (ACL), reconstructing the resource distribution of Load Balancers or revealing of Moving Target Defense techniques.In this paper we introduce a new attack vector on SDN by showing how the detailed composition of flow rules can be reconstructed by network users without any prior knowledge of the SDN controller or its architecture. To our best knowledge, in SDN, such reconnaissance techniques have not been considered so far. We introduce SDNMap, an open-source scanner that is able to accurately reconstruct the detailed composition of flow rules by performing active probing and listening to the network traffic. We demonstrate in a number of real-world SDN applications that this ability provides adversaries with a significant attack advantage and discuss ways to prevent the introduced reconnaissance techniques. Our SDNMap scanner is able to reconstruct flow rules between network endpoints with an accuracy of over 96%.

The essential part of an SDN-based network are flow rules that enable network elements to steer and control the traffic and deploy policy enforcement points with a fine granularity at any entry-point in a network. Such applications, implemented with the usage of OpenFlow rules, are already integral components of widely used SDN controllers such as Floodlight or OpenDayLight. The implementation details of network policies are reflected in the composition of flow rules and leakage of such information provides adversaries with a significant attack advantage such as bypassing Access Control Lists (ACL), reconstructing the resource distribution of Load Balancers or revealing of Moving Target Defense techniques.In this demo [4, 5] we present our open-source scanner SDNMap and demonstrate the findings discussed in the paper "Adversarial Network Forensics in Software Defined Networking" [6]. On two real world examples, Floodlight's Access Control Lists (ACL) and Floodlight's Load Balancer (LBaaS), we show that severe security issues arise with the ability to reconstruct the details of OpenFlow rules on the data-plane.

The virtual force approach is at the basis of many solutions proposed for deploying mobile sensors. In this paper we study the vulnerabilities of this approach. We show that by compromising a few mobile sensors, an attacker can influence the movement of other sensors and prevent the achievement of the network coverage goals. We introduce an attack, called opportunistic movement, and give an analytical study of its efficacy. We show that in a typical scenario this attack can reduce coverage by more than 50 percent, by only compromising a 7 percent of the nodes. We propose two algorithms to counteract the above mentioned attack, DRM and SecureVF. DRM is a light-weight algorithm which randomly repositions sensors from overcrowded areas. SecureVF requires a more complex coordination among sensors but, unlike DRM, it enables detection and identification of malicious sensors. We investigate the performance of DRM and SecureVF through simulations. We show that DRM can significantly reduce the effects of the attack, at the expense of an increase in the energy consumption due to additional movements. By contrast, SecureVF completely neutralizes the attack and allows the achievement of the coverage goals of the network even in the presence of localization inaccuracies.

Backup paths are widely used in IP networks to protect IP links from failures. However, existing solutions such as the commonly used independent model and Shared Risk Link Group (SRLG) model do not accurately reflect the correlation between IP link failures, and thus may not choose reliable backup paths. We propose a cross-layer approach for minimizing routing disruption caused by IP link failures. We develop a probabilistically correlated failure (PCF) model to quantify the impact of IP link failure on the reliability of backup paths. With the PCF model, we propose an algorithm to choose multiple reliable backup paths to protect each IP link. When an IP link fails, its traffic is split onto multiple backup paths to ensure that the rerouted traffic load on each IP link does not exceed the usable bandwidth. We evaluate our approach using real ISP networks with both optical and IP layer topologies. Experimental results show that two backup paths are adequate for protecting a logical link. Compared with existing works, the backup paths selected by our approach are at least 18 percent more reliable and the routing disruption is reduced by at least 22 percent. Unlike prior works, the proposed approach prevents the rerouted traffic from interfering with normal traffic.

We study a data dissemination scenario in which data items are to be transmitted to mobile clients via one of the stationary data access points (APs) that the clients pass by en route to their destinations. The scheduler dedicates sequences of consecutive timeslots of an AP to downloading a data item to a client during the time window in which it is in range, which corresponds to assigning a job (the client's download) to a machine (the AP) among many. The transmission rate chosen for each assignment partly corresponds to setting a machine's speed, but it also has subtler effects. The APs may control transmission power to tune its transmission range making sure that no interference occurs with neighboring APs' transmissions. The problem is a generalization of an already NP-hard parallel-machine scheduling problem in which jobs' release times and deadlines depend on the machine to which they are assigned. We define this joint timeslot, power control, and rate assignment problem formally and apply both new algorithms and adaptations of existing algorithms to it. We evaluate these algorithms through simulations which show that our proposed algorithms achieve near-optimal throughput.

The proliferation and ever-increasing capabilities of mobile devices such as smart phones give rise to a variety of mobile sensing applications. This paper studies how an untrusted aggregator in mobile sensing can periodically obtain desired statistics over the data contributed by multiple mobile users, without compromising the privacy of each user. Although there are some existing works in this area, they either require bidirectional communications between the aggregator and mobile users in every aggregation period, or have high-computation overhead and cannot support large plaintext spaces. Also, they do not consider the Min aggregate, which is quite useful in mobile sensing. To address these problems, we propose an efficient protocol to obtain the Sum aggregate, which employs an additive homomorphic encryption and a novel key management technique to support large plaintext space. We also extend the sum aggregation protocol to obtain the Min aggregate of time-series data. To deal with dynamic joins and leaves of mobile users, we propose a scheme that utilizes the redundancy in security to reduce the communication cost for each join and leave. Evaluations show that our protocols are orders of magnitude faster than existing solutions, and it has much lower communication overhead.

We consider video processing to detect complex activities in a distributed network consisting of mobile devices and video-cloud servers. To address varying task requirements and resource-constraints of mobile devices, we consider fragmentation of the video processing workflow. Fragmentation of the workflow allows for the mobile device to filter video clips based on metadata, process portions of the clips, and offload other videos to video-cloud servers. In certain situations, the cloud may require access to the raw video even if the video is processed by a mobile device. Through the use of resizing the image and a top-k analysis, we explore various quality of information metrics in the addressing of complex activity detection that consider energy usage of the mobile devices and the quality of detecting objects in terms of completeness and timeliness.

In infrastructure-lacking environments, like military areas of operation, the intermittent availability of backhaul networks leads to greater reliance on peer to peer data exchange. In such settings, mobile nodes use Delay Tolerant Network (DTN) protocols for exchanging location specific data. High transmission delay, packet loss and intermittent connectivity increases the need for efficient data transmission in such scenarios. In this paper, we evaluate network coding for efficient data exchange between mobile wireless nodes under the presence of an intermittent backhaul network, like a satellite or cellular link. We analyze the upper bound of savings achievable with network coding for single-hop packet transmission, and introduce a network coding algorithm focused on reducing the required number of packet transmissions in such a setting. Evaluation of our proposed data exchange protocol shows how network coding can be applied to reduce transmission delay and minimize the dependency on an intermittent backhaul network in a communication scenario typical of a military environment.

Opportunistic mobile networks consisting of intermittently connected mobile devices have been exploited for various applications, such as computational offloading and mitigating cellular traffic load. Different from existing work, in this paper, we focus on cooperatively offloading data among mobile devices to maximally improve the probability of data delivery from a mobile device to an intermittently connected remote server or data center within a given time constraint, which is referred to as the cooperative offloading problem. Unfortunately, cooperative offloading is NP-hard. To this end, a heuristic algorithm is designed based on the proposed probabilistic framework, which provides the estimation of the probability of successful data delivery over the opportunistic path, considering both data size and contact duration. Due to the lack of global information, a distributed algorithm is further proposed. The performance of the proposed approaches is evaluated based on both synthetic networks and real traces, and simulation results show that cooperative offloading can significantly improve the data delivery probability and the performance of both heuristic algorithm and distributed algorithm outperforms other approaches.

Densely-deployed femtocell networks are used to enhance wireless coverage in public spaces like office buildings, subways, and academic buildings. These networks can increase throughput for users, but edge users can suffer from co-channel interference, leading to service outages. This paper introduces a distributed algorithm for network configuration, called Radius Reduction and Scheduling (RRS), to improve the performance and fairness of the network. RRS determines cell sizes using a Voronoi-Laguerre framework, then schedules users using a scheduling algorithm that includes vacancy requests to increase fairness in dense femtocell networks. We prove that our algorithm always terminate in a finite time, producing a configuration that guarantees user or area coverage. Simulation results show a decrease in outage probability of up to 50%, as well as an increase in Jain's fairness index of almost 200%.

Quality of information (QoI) provides a context-dependent measure of the utility that a network delivers to its users by incorporating non-traditional information attributes. Quickly and easily predicting performance and limitations of a network using QoI metrics is a valuable tool for network design. Even more useful is an understanding of how network components like topology, bandwidth, and protocols, impact these limitations. In this paper, we develop a QoI-based framework that can provide accurate estimates for limitations on network size and achievable QoI requirements, focusing on completeness and timeliness. We extend this framework to model competing flows and data loads as random variables to capture the stochastic nature of real networks. We show that our framework can provide a characterization of delays for satisfied queries to further analyze performance when some late arrivals are acceptable. Analysis shows that the large tradeoffs exist between network parameters, such as QoI requirements, topology, and network size. Simulation results also provide evidence that the developed framework can estimate network limits and delays with high accuracy. Finally, this paper also introduces scalably feasible QoI regions , which provide upper bounds on QoI requirements that can be supported for certain network applications.

Mobility management is a key aspect of designing and evaluating protocols for Mobile Ad Hoc Networks (MANETs). The high mobility of nodes in a MANET constantly causes the network topology to change. Mobility patterns of nodes have a direct effect on fundamental network characteristics, such as path length, neighborhood size, and link stability. Consequently, the network performance is strongly affected by the nature of mobility patterns. While evaluating protocols for a specific MANET application, it becomes imperative to use a mobility model that is able to capture the movement of nodes in an accurate manner. The objective of this work is to produce mobility models that are able to describe tactical mobility in military applications of MANETs. We provide models of four tactical scenarios, show that these models are accurate compared to synthetic traces, and that when used to evaluate network protocols, they provide different conclusions than when using generic mobility models.

Both bandwidth and energy become important resource constraints when multihop wireless networks are used to transport high-data-rate traffic for a moderately long duration. In such networks, it is important to control the traffic rates to not only conform to the link capacity bounds, but also to ensure that the energy of battery-powered forwarding nodes is utilized judiciously to avoid premature exhaustion (i.e., the network lasts as long as the applications require data from the sources) without being unnecessarily conservative (i.e., ensuring that the applications derive the maximum utility possible). Unlike prior work that focuses on the instantaneous distributed optimization of such networks, we consider the more challenging question of how such optimal usage of both link capacity and node energy may be achieved over a time horizon. Our key contributions are twofold. We first show how the formalism of optimal control may be used to derive optimal resource usage strategies over a time horizon, under a variety of both deterministic and statistically uncertain variations in various parameters, such as the duration for which individual applications are active or the time-varying recharge characteristics of renewable energy sources (e.g., solar cell batteries). In parallel, we also demonstrate that these optimal adaptations can be embedded, with acceptably low signaling overhead, into a distributed, utility-based rate adaptation protocol. Simulation studies, based on a combination of synthetic and real data traces, validate the close-to-optimal performance characteristics of these practically realizable protocols.

In many situations and domains it is important to deliver information to personnel as they work in the field. We consider a specialized content distribution application in wireless mesh networks. When a new mission arrives--for example, when a fire alarm sounds--data is pushed to storage nodes at the mission site where it may be retrieved locally by responding personnel (e.g., police, firefighters, paramedics, government officials, and the media). It is important that information is available at low latency, when requested or pulled by the personnel. The total latency experienced will be a combination of the push delay (if the personnel arrive at the mission site before all the data can be pushed), and the pull delay. Each delay component will in turn be a function of (1) the hop distance traveled by the data when pushed or pulled and (2) any congestion on the links. In this paper, we define algorithms and protocols that trade-off the push and pull latencies depending on the type of application. Our goal is to choose a storage node assignment minimizing the total latency-based cost. We start with a simple model in which cost is a function of distance, and then extend the model, explicitly taking congestion into account. Since the problem is NP-hard even to approximate, our focus is on developing efficient algorithms and distributed protocols that can be easily deployed in wireless mesh networks. In NS2 simulations, we find that our heuristic algorithms achieve, on average, a cost within at most 15% of the optimum.

This article develops a utility-based optimization framework for resource sharing by multiple competing missions in a mission-oriented wireless sensor network (WSN) environment. Prior work on network utility maximization (NUM) based optimization has focused on unicast flows with sender-based utilities in either wireline or wireless networks. In this work, we develop a generalized NUM model to consider three key new features observed in mission-centric WSN environments: i) the definition of the utility of an individual mission (receiver) as a joint function of data from multiple sensor sources; ii) the consumption of each sender's (sensor) data by multiple missions; and iii) the multicast-tree-based dissemination of each sensor's data flow, using link-layer broadcasts to exploit the “wireless broadcast advantage” in data forwarding. We show how a price-based, distributed protocol (WSN-NUM) can ensure optimal and proportionally fair rate allocation across multiple missions, without requiring any coordination among missions or sensors. We also discuss techniques to improve the speed of convergence of the protocol, which is essential in an environment as dynamic as the WSN. Further, we analyze the impact of various network and protocol parameters on the bandwidth utilization of the network, using a discrete-event simulation of a stationary wireless network. Finally, we corroborate our simulation-based performance results of the WSN-NUM protocol with an implementation of an 802.11b network.

In sensor networks applied to monitoring applications, individual sensors may perform preassigned or on-demand tasks, or missions. Data updates (info-pages) may be sent to sensors from a command center, via a time-division broadcast channel. Sensors are normally put in sleep mode when not actively listening, in order to conserve energy in their batteries. Hence, a schedule is required that specifies when sensors should listen for updates and when they should sleep. The performance of such a schedule is evaluated based on data-related costs and sensor-related costs. Data-related costs reflect the obsoleteness of current sensor data, or the delay while sensors wait for updated instructions. Sensor-related costs reflect the energy that sensors consume while accessing the broadcast channel and while switching between the active and sleeping modes (rebooting). Our goal is a schedule with the minimum total cost. Previous related work has explored data-related costs, but listening cost has been addressed only under the assumption that the rebooting operation is free. This paper formulates a new cost model, which recognizes the cost of sensor rebooting. We derive an optimal schedule for the single-sensor setting. We proceed to consider schedules of multiple sensors; we formulate a mathematical program to find an optimal fractional schedule for this setting and provide a solution to the lower bound. Several heuristics for scheduling multiple sensors are introduced and analyzed, and various tradeoffs among the cost factors are demonstrated.

In this paper, we address the problem of deploying heterogeneous mobile sensors over a target area. Traditional approaches to mobile sensor deployment are specifically designed for homogeneous networks. Nevertheless, network and device homogeneity is an unrealistic assumption in most practical scenarios, and previous approaches fail when adopted in heterogeneous operative settings. For this reason, we introduce VorLag, a generalization of the Voronoi-based approach which exploits the Laguerre geometry. We theoretically prove the appropriateness of our proposal to the management of heterogeneous networks. In addition, we demonstrate that VorLag can be extended to deal with dynamically generated events or uneven energy depletion due to communications. Finally, by means of simulations, we show that VorLag provides a very stable sensor behavior, with fast and guaranteed termination and moderate energy consumption. We also show that VorLag performs better than its traditional counterpart and other methods based on virtual forces.

When a sensor network is deployed, it is typically required to support multiple simultaneous missions. Schemes that assign sensing resources to missions thus become necessary. In this article, we formally define the sensor-mission assignment problem and discuss some of its variants. In its most general form, this problem is NP-hard. We propose algorithms for the different variants, some of which include approximation guarantees. We also propose distributed algorithms to assign sensors to missions which we adapt to include energy-awareness to extend network lifetime. Finally, we show comprehensive simulation results comparing these solutions to an upper bound on the optimal solution.

In this paper, we investigate how to network smart-phones for providing communications in disaster recovery. By bridging the gaps among different kinds of wireless networks, we have designed and implemented a system called TeamPhone, which provides smartphones the capabilities of communications in disaster recovery. Specifically, TeamPhone consists of two components: a messaging system and a self-rescue system. The messaging system integrates cellular networking, ad-hoc networking and opportunistic networking seamlessly, and enables communications among rescue workers. The self-rescue system energy-efficiently groups the smartphones of trapped survivor and sends out emergency messages so as to assist rescue operations. We have implemented TeamPhone as a prototype application on the Android platform and deployed it on off-the-shelf smartphones. Experiment results show that TeamPhone can properly fulfill communication requirements and greatly facilitate rescue operations in disaster recovery.

In this paper, we investigate how to network smartphones for providing communications in disaster recovery. By bridging the gaps among different kinds of wireless networks, we have designed and implemented a system called TeamPhone, which provides smartphones the capabilities of communications in disaster recovery. Specifically, TeamPhone consists of two components: A messaging system and a self-rescue system. The messaging system integrates cellular networking, ad-hoc networking, and opportunistic networking seamlessly, and enables communications among rescue workers. The self-rescue system groups, schedules, and positions the smartphones of trapped survivors. Such a group of smartphones can cooperatively wake up and send out emergency messages in an energy-efficient manner with their location and position information so as to assist rescue operations. We have implemented TeamPhone as a prototype application on the Android platform and deployed it on off-the-shelf smartphones. Experimental results demonstrate that TeamPhone can properly fulfill communication requirements and greatly facilitate rescue operations in disaster recovery.

Mobile sensor networks are the most promising solution to cover an Area of Interest (AoI) in safety critical scenarios. Mobile devices can coordinate with each other according to a distributed deployment algorithm, without resorting to human supervision for device positioning and network configuration. In this paper, we focus on the vulnerabilities of the deployment algorithms based on Voronoi diagrams to coordinate mobile sensors and guide their movements. We give a geometric characterization of possible attack configurations, proving that a simple attack consisting of a barrier of few compromised sensors can severely reduce network coverage. On the basis of the above characterization, we propose two new secure deployment algorithms, named SecureVor and Secure Swap Deployment (SSD). These algorithms allow a sensor to detect compromised nodes by analyzing their movements, under different and complementary operative settings. We show that the proposed algorithms are effective in defeating a barrier attack, and both have guaranteed termination. We perform extensive simulations to study the performance of the two algorithms and compare them with the original approach. Results show that SecureVor and SSD have better robustness and flexibility and excellent coverage capabilities and deployment time, even in the presence of an attack.

Multi-hop relaying in cellular networks can greatly increase capacity and performance by exploiting the best available links to a base station. We envision an environment in which relay networks are dynamically formed when performance on the radio access network is degraded and then dissolved when the performance improves or the radio spectrum on which the relay network is operating is reclaimed. Each relay network operates on a different frequency band. Likewise, a relay network may channelize its frequency band to offer non-interfering links among the mobile nodes within a single relay network. We propose a set of algorithms used to form such relay networks on-demand. Each algorithm provides a simple and distributed frequency assignment scheme. We also propose two enhancements to improve network throughput of resulting relay networks. We evaluate these algorithms in terms of the overhead of the relay network formation. The evaluation results show that having nodes outmost from the BS initiate route discovery first is the best approach for reducing the formation overhead. The results also show that there is a large increase in throughput when using multiple frequencies in a relay network. Further, the performance of the network using multiple frequencies based on our simple frequency assignment is very close to that of a network using optimal frequency assignment.

Provisioning the capacity of wireless networks is difficult when peak load is significantly higher than average load, for example, in public spaces like airports or train stations. Service providers can use femtocells and small cells to increase local capacity, but deploying enough femtocells to serve peak loads requires a large number of femtocells that will remain idle most of the time, which wastes a significant amount of power. To reduce the energy consumption of over-provisioned femtocell networks, we formulate a femtocell selective activation problem, which we formalize as an integer nonlinear optimization problem. Then we introduce Green Femto, a distributed femtocell selective activation algorithm that deactivates idle femtocells to save power and activates them on-the-fly as the number of users increases. We prove that Green Femto converges to a locally Pareto optimal solution and demonstrate its performance using extensive simulations of an LTE wireless system. Overall, we find that Green Femto requires up to 55% fewer femtocells to serve a given user load, relative to an existing femtocell power-saving procedure, and comes within 15% of a globally optimal solution.

There has been much recent attention on using wireless relay networks to forward data from mobile nodes to a base station. This network architecture is motivated by performance improvements obtained by leveraging the highest quality links to a base station for data transfer. With the advent of agile radios it is possible to improve the performance of relay networks through intelligent frequency assignments. First, it is beneficial if the links of the relay network are orthogonal with respect to each other so that simultaneous transmission on all links is possible. Second, diversity can be added to hops in the relay network to reduce error rates. In this paper we present algorithms for forming such relay networks dynamically. The formation algorithms support intelligent frequency assignments and diversity setup. Our results show that algorithms that order the sequence in which nodes join a relay network carefully, achieve the highest amount of diversity and hence best performance.

In this paper we present a framework for Quality of Information (QoI)-aware networking. QoI quantifies how useful a piece of information is for a given query or application. Herein, we present a general QoI model, as well as a specific example instantiation that carries throughout the rest of the paper. In this model, we focus on the tradeoffs between precision and accuracy. As a motivating example, we look at traffic video analysis. We present simple algorithms for deriving various traffic metrics from video, such as vehicle count and average speed. We implement these algorithms both on a desktop workstation and less-capable mobile device. We then show how QoI-awareness enables end devices to make intelligent decisions about how to process queries and form responses, such that huge bandwidth savings are realized.

In wireless networks, mobile hosts must update the network with their current location in order to get packets delivered. Paging facilitates efficient power management at the mobile host by allowing the host to update the network less frequently at the cost of providing the network with only approximate location information. The network determines the exact location of a mobile host through paging before delivering packets destined to the mobile host. In this paper, we propose the concept of paging as an IP service. IP paging enables a common infrastructure and protocol to support the different wireless interfaces such as CDMA, GPRS, wireless LAN, avoiding the duplication of several application layer paging implementations and the inter-operability issues that exist today. We present the design, implementation, and detailed qualitative and quantitative evaluation, using measurements and simulation, of three IP-based paging protocols for mobile hosts.

Images/videos are often uploaded in situations like disasters. This can tax the network in terms of increased load and thereby upload latency, and this can be critical for response activities. In such scenarios, prior work has shown that there is significant redundancy in the content (e.g., similar photos taken by users) transferred. By intelligently suppressing/deferring transfers of redundant content, the load can be significantly reduced, thereby facilitating the timely delivery of unique, possibly critical information. A key challenge here however, is detecting “what content is similar,” given that the content is generated by uncoordinated user devices. Toward addressing this challenge, we propose a framework, wherein a service to which the content is to be uploaded first solicits metadata (e.g., image features) from any device uploading content. By intelligently comparing this metadata with that associated with previously uploaded content, the service effectively identifies (and thus enables the suppression of) redundant content. Our evaluations on a testbed of 20 Android smartphones and via ns3 simulations show that we can identify similar content with a 70% true positive rate and a 1% false positive rate. The resulting reduction in redundant content transfers translates to a latency reduction of 44 % for unique content.

Large-scale enterprise femtocell deployments can significantly impact the performance and energy consumption of the underlying wireless networks that support them. Naive femtocell deployments can lead to higher overall network energy usage, while optimized femtocell deployments can increase total network connectivity and reduce macrocell energy consumption. This paper approaches femtocell deployment as a combinatorial optimization problem. We first consider accelerated greedy algorithms using one of two metrics: femtocell coverage and area spectral efficiency. Then, motivated by an analysis of the strengths and weaknesses of each metric, we introduce an algorithm that takes the weighted sum of both metrics. We evaluate our algorithms using extensive simulations, and find that our weighted sum algorithm decreases outage probability by up to 30% relative to existing greedy approaches. Furthermore, our algorithm can lead to a reduction in total network energy usage by up to 14%.