Following the expansion of IoT systems, spanning from devices to cloud backends, reported IoT CVE vulnerabilities have increased at an alarming pace. Since most IoT attacks exploit known vulnerabilities, understanding known vulnerabilities is vital for defense and security research. In this work, we systematize the prior research on studying IoT vulnerabilities, revealing the absence of consistent IoT definitions, reliable and scalable classification methodologies, and high-quality IoT CVE datasets. To overcome these limitations, we design LLIoT, a novel and LLM-assisted approach that systematically and automatically distinguishes IoT-specific CVEs at large scale, enabling in-depth under-standing of IoT vulnerabilities. First, leveraging the systematization knowledge from the literature, we derive a four-layer IoT ecosystem taxonomy and define classification criteria for distinguishing IoT CVEs. Then, using an expert-validated ground-truth dataset, we demonstrate that LLMs can reliably distinguish IoT from non-IoT CVEs with a high accuracy of 95%, outperforming humans by avoiding cognitive errors and gaps in domain knowledge. Applying LLIoT to CVEs from 2013-2024, we build a dataset of 15,116 IoT-specific vulnerabilities, of which 8,368 are newly classified with respect to previous datasets. Using this dataset, which we share with the research community for further research and reproducibility, we characterize how IoT vulnerabilities differ from traditional IT vulnerabilities. Upon our observation, we provide actionable recommendations for responsible stakeholders.

Besides being the de facto routing protocol of the Internet, the Border Gateway Protocol (BGP) has also been used for mitigating Distributed Denial of Service (DDoS) attacks for many years. In such situations, victims of DDoS attacks use BGP to redirect attack traffic to a “scrubber” outside their network, which separates clean traffic from DDoS traffic and forwards the former to the victim’s network. While there exist many BGP-based DDoS scrubbing providers, their adoption on the global Internet remains unstudied. This paper aims to fill this gap by identifying and characterizing Autonomous Systems (ASes) and prefixes protected by five of the leading scrubbing providers, using AS path patterns in public BGP routing data. Our study focuses on scrubbers that allow their protected ASes to originate their prefixes themselves. We find that the percentage of ASes using this kind of protection has increased almost three times (from 0.7% to 2% and from 464 ASes to 1,730 ASes) between 2020 and 2024. Similarly, the percentage of protected prefixes has also increased three times in the same period, from 0.3% to 0.9% and from 3,154 to 12,362 prefixes, across both IPv4 and IPv6. Globally, we observe a higher adoption rate among financial institutions, while adoption remains low among educational institutions. We believe our insights will be useful for individual AS operators to find the transit providers or peers that are DDoS-protected. It might also be useful for (national) policy-makers to incentivize the adoption of DDoS protection services and for researchers studying the phenomenon of DDoS scrubbing.

Cell-free massive MIMO (mMIMO) promises to enhance coverage, spectral efficiency, and robustness in dense urban environments by leveraging a distributed approach where multiple access points (APs) cooperate to serve each user. While previous studies have primarily focused on fiber-based fronthaul solutions, wireless fronthaul is gaining traction due to its cost-effectiveness and deployment flexibility. This paper investigates the impact of millimeter-wave wireless fronthaul on the energy efficiency (EE) of cell-free mMIMO, with a specific focus on AP deployment on existing urban infrastructure, such as lampposts. With a case study of three mobile network operators in Amsterdam, we analyze the effects of AP density, AP cluster size, and power allocation strategies on network performance. Our findings reveal that an AP density of approximately 43 APs/km2optimally balances sum rate and power consumption, while an effective AP clustering and power allocation strategy further enhances EE. Our findings provide data-driven insights for mobile network operators (MNOs) to optimize their network expansion and deployment in urban environments.

Since the spectrum below 6 GHz bands is insufficient to meet the high bandwidth requirements of 5G use cases, 5G networks expand their operation to mmWave bands. However, operation at these bands has to cope with a high penetration loss and susceptibility to blocking objects. Beamforming and multi-connectivity (MC) can together mitigate these challenges. But, to design such an optimal user association scheme leveraging these two features is non-trivial and computationally expensive. Previous studies either considered a fixed MC degree for all users or overlooked beamforming. Driven by the question what is the optimal degree of MC for each user in a mmWave network, we formulate a user association scheme that maximizes throughput considering beam formation and MC. Our numerical analysis shows that there is no one-size-fits-all degree of optimal MC; it depends on the number of users, their rate requirements, locations, and the maximum number of active beams at a BS. Based on the optimal association, we design beam-align : an efficient heuristic with polynomial-time complexity O ( | U | log | U | ) , where | U | is the number of users. Moreover, beam-align only uses local BS information - i.e. the received signal quality at the user. Differing from prior works, beam-align considers beamforming, multiconnectivity and line-of-sight probability. Via simulations, we show that beam-align performs close to optimal in terms of per-user capacity and satisfaction while it outperforms frequently-used signal-to-interference-and-noise-ratio based association schemes. We then show that beam-align has a robust performance under various challenging scenarios: the presence of blockers, rain, and clustered users.

With the growing number of Federated Learning (FL) applications in smart homes, it becomes crucial to manage communication and computation resources within the smart home so that FL applications can complete their training on time. While computation offloading has relieved the challenge of timely completion of applications in case of high competition for local resources, privacy of the smart home data remains a critical concern. This paper introduces ERAFL, a resource allocation and computation offloading algorithm running on a home gateway. Unlike privacy-oblivious prior works, ERAFL considers privacy-sensitivity level of FL training data in offloading decision, prioritizing local processing of more sensitive data, e.g., biological personal data. Moreover, in case of insufficient local resources, ERAFL offloads a part of data and accelerates training by leveraging parallel training on the cloud and the edge device. It also imposes limits on the amount of offloaded data or performs the training either locally or remotely to ensure model accuracy. Our simulation results show that ERAFL can satisfy more FL training tasks and reduce data privacy leakage in comparison to the baselines that do not consider partial offloading, privacy sensitivity of application data or resource allocation.

5G networks promise to deliver an unprecedented performance that can accommodate novel services with stringent Quality of Service (QoS) requirements that were not possible with previous generations of networks. Edge Computing plays a fundamental role by providing computing resources closer to the user, reducing round trip times. However, the deployment of edge computing poses new challenges, including the energy footprint of a potentially large number of servers. Even in idle state, these servers consume a significant amount of energy, which is worth considering for reducing their energy footprint. In cloud computing environments, server shutdown during low-demand periods is a typical energy-saving strategy. However, this approach has received less attention in edge computing due to the strict latency requirements of its use cases. This work presents ODESA, an edge server shutdown strategy with polynomial time complexity that provides a tradeoff between the idle energy consumption of the edge servers and energy consumed by the backhaul to route requests to active servers. Our numerical investigation shows that thanks to the reduction in idle energy consumption, ODESA reduces the total consumption by 42% over the common always-on approach during low-demand periods and 11% over 24 hours, all while meeting the latency requirements of the applications.

While advances in wireless networks enable novel services with previously unreachable latency guarantees, edge computing becomes essential for delivering computing resources close to the users and meeting the strict latency requirements. However, addressing the energy footprint of computing resources is crucial amid the pressing sustainability concerns. The energy consumption of idle resources accounts for a significant part of the total energy footprint. While server shutdown during low-demand periods is common in cloud computing, it is challenging to determine which edge servers to shut down and how to route requests due to the stringent latency requirements of the applications. Thus, this work formulates an optimal orchestration policy to minimize the energy consumption of the edge computing infrastructure and presents LESS-ON, a strategy with a polynomial time complexity that reduces the operational energy footprint of edge computing by shutting down edge servers during low-demand periods. In contrast to previous studies, LESS-ON considers the energy requirements associated with routing requests to the designated edge servers. Our numerical evaluation shows that LESS-ON reduces the total consumption by 42% with respect to the common always-on approach during low-demand periods and by 35% over 24 h, all while meeting latency requirements.

Cellular networks have become one of the critical infrastructures, as many services depend increasingly on wireless connectivity. Therefore, it is important to quantify the resilience of existing cellular network infrastructures against potential risks, ranging from natural disasters to security attacks, that might occur with a low probability but can lead to severe disruption of the services. In this paper, we combine models with public data from national bodies on mobile network operator (MNO) infrastructures, population distribution, and urbanity level to assess the coverage and capacity of a cellular network at a country scale. Our analysis offers insights on the potential weak points that need improvement to ensure a low fraction of disconnected population (FDP) and high fraction of satisfied population (FSP). As a resilience improvement approach, we investigate in which regions and to what extent each MNO can benefit from infrastructure sharing or national roaming, i.e., all MNOs act as a single national operator. As our case study, we focus on Dutch cellular infrastructure and model risks as random failures and correlated failures in a geographic region. Our analysis shows that there is a wide performance difference across MNOs and geographic regions in terms of FDP and FSP. However, national roaming consistently offers significant benefits in some regions, e.g., up to 13% improvement in FDP and up to 55% in FSP when the networks function without any failures. We then show that a similar performance improvement can be obtained by partial implementation of national roaming.

Edge computing has transformed cellular networks, offering fast response times by moving computing resources to the network's edge. This not only reduces the burden on the Wide Area Network (WAN) but also enables latency-sensitive applications. However, the widespread deployment of edge computing raises concerns regarding its sustainability. In this work, we present MintEDGE, a simulation framework that models a fully configurable edge-enabled cellular network. MintEDGE empowers researchers and practitioners to design and assess energy-saving strategies for edge computing. We discuss the details of the simulator and its customizable elements like user mobility, the possibility to use predictive workload algorithms, and diverse application scenarios at scale. MintEDGE is released under a permissive MIT license.

Due to the increasing volume of data generated by sensors in modern cars, the need for high data rate links rises in vehicular networking. A promising way to achieve this is using mmWave communications, although beamforming is needed to overcome the high propagation losses at these frequencies. In addition, relaying might be needed to extend the coverage to larger distances for the delivery of a message in a specific geographical area, called geocasting. Moreover, reaching multiple receivers at once via multicasting can be achieved by using a wider antenna beamwidth, which comes at the cost of transmission range, while spatial sharing can be exploited using narrow beams. This paper investigates if using multicasts is beneficial for routing and scheduling of mmWave geocasts that need to be delivered before a timeout. We consider a non-time-slotted system with realistic antenna model and multiple data rates, for which we seek an optimal solution by modeling it as a mixed-integer linear program. Our numerical evaluations show that using multicasts is especially advantageous in scenarios with multiple highway lanes. Furthermore, we devise a heuristic algorithm that efficiently finds a route and creates a transmission schedule for a geocast. Several methods to include multicast links are evaluated, of which some consistently outperform the unicast-only method.

In the early days of cloud computing, datacenters were sparsely deployed at distant locations far from end-users with high end-to-end communication latency. However, today’s cloud datacenters have become more geographically spread, the bandwidth of the networks keeps increasing, pushing the end-users latency down. In this paper, we provide a comprehensive cloud reachability study as we perform extensive global client-to-cloud latency measurements towards 189 datacenters from all major cloud providers. We leverage the well-known measurement platform RIPE Atlas, involving up to 8500 probes deployed in heterogeneous environments, e.g., home and offices. Our goal is to evaluate the suitability of modern cloud environments for various current and predicted applications. We achieve this by comparing our latency measurements against known human perception thresholds and are able to draw inferences on the suitability of current clouds for novel applications, such as augmented reality. Our results indicate that the current cloud coverage can easily support several latency-critical applications, like cloud gaming, for the majority of the world’s population.

Edge computing has gained attention from both academia and industry by pursuing two significant challenges: 1) moving latency critical services closer to the users, 2) saving network bandwidth by aggregating large flows before sending them to the cloud. While the rationale appeared sound at its inception almost a decade ago, several current trends are impacting it. Clouds have spread geographically reducing end-user latency, mobile phones? computing capabilities are improving, and network bandwidth at the core keeps increasing. In this paper, we scrutinize edge computing, examining its outlook and future in the context of these trends. We perform extensive client-to-cloud measurements using RIPE Atlas, and show that latency reduction as motivation for edge is not as persuasive as once believed; for most applications the cloud is already 'close enough' for majority of the world's population. This implies that edge computing may only be applicable for certain application niches, as opposed to a general-purpose solution.

As the radio spectrum has become the bottleneck resource with increasing volume of mobile data and ultra-dense network deployments, it is crucial to use spectrum more flexibly in time, space, and frequency dimensions. However, higher efficiency in spectrum usage facilitated by flexible spectrum allocation comes with a cost, namely the increased complexity of spectrum monitoring and management. Identifying the transmitters is at the interest of particularly spectrum enforcement authorities to ensure that spectrum is used as intended by the legitimate users of the spectrum. For a scalable, efficient, and highly-accurate operation, we propose a crowd-sensing based solution where sensing devices report their measured receive power levels to a central entity which later fuses the collected information for localizing an unknown number of transmitters. Our solution, referred to as DeepTxFinder, leverages deep learning to handle many sources of uncertainty in the operation environment: namely number of transmitters, their transmission power levels, and channel conditions (shadowing). Using deep-learning, DeepTxFinder distinguishes itself from the prior state-of-the art which requires knowledge of the number and transmission power of transmitters or require the transmitters to be well separated in space by tens to hundreds of meters making them ill-suited for application in expected ultra-dense deployment of small-cells. Moreover, we propose a tiling-based approach to increase the scalability of our proposal by reducing the computational complexity. Our simulation studies show that DeepTxFinder can provide a high detection accuracy even only by collecting data from a very small number of sensors. More specifically, with 1 %-2 % sensor density DeepTxFinder can estimate the number of transmitters and their locations with high probability which proves that sparse sensing is feasible.

Software-Defined Networking promises to deliver a more manageable network whose behaviour could be easily changed using applications written in high-level declarative languages running on top of a logically centralized control plane resulting, on the one hand, in the mushrooming of complex point solutions to very specific problems and, on the other hand, in the creation of a multitude of network configuration options. This fact is especially true for 802.11-based Software-Defined WLANs (SD-WLANs). It is our standpoint that to tame this increase in complexity, future SD-WLANs must follow an Artificial Intelligence (AI) native approach. In this paper we present aiOS, an AI-based Operating System for SD-WLANs. Then, we use aiOS to implement several Machine Learning (ML) models for user-adaptive frame length selection in SD-WLANs. An extensive performance evaluation carried out on a real-world testbed shows that this approach improves the aggregated network throughput by up to 55%. Finally, we release the entire implementation including the controller, the ML models, and the programmable data-path under a permissive license for academic use.

Despite exhibiting very high theoretical data rates, in practice, the performance of LTE-U/LAA and WiFi networks is severely limited under cross-technology coexistence scenarios in the unlicensed 5GHz band. As a remedy, recent research shows the need for collaboration and coordination among colocated networks. However, enabling such collaboration requires an information exchange that is hard to realize due to completely incompatible network protocol stacks. We propose OfdmFi, the first cross-technology communication scheme that enables direct bidirectional over-the-air communication between LTE-U/LAA and WiFi with minimal overhead to their legacy transmissions. Requiring neither hardware nor firmware changes in commodity technologies, OfdmFi leverages the standard-compliant possibility of generating message-bearing power patterns, similar to punched cards from the early days of computers, in the time-frequency resource grid of an OFDM transmitter which can be cross-observed and decoded by a heterogeneous OFDM receiver. As a proof-of-concept, we have designed and implemented a prototype using commodity devices and SDR platforms. Our comprehensive evaluation reveals that OfdmFi achieves robust bidirectional CTC between both systems with a data rate of up to 84kbps, which is more than 125× faster than state-of-the-art.

Cross-Technology Communication (CTC) allows direct message exchange between devices with different (i.e., incompatible) wireless communication standards. CTC is particularly suitable to allow for coordination between heterogeneous devices sharing the same spectrum, as in the Internet of Things. Existing research on CTC has focused on enabling communications for diverse technologies with the goal of achieving a high throughput. However, it did not address how to establish a link suitable for CTC, which is necessary for successful data exchange. This article specifically addresses such a problem by introducing CTC-CEM (CTC Channel Establishment with Multiple nodes), a scheme to establish a CTC channel involving the use of multiple nodes in a network. CTC-CEM employs duty-cycling and leverages network density to reduce energy consumption, while keeping a low discovery latency. In particular, CTC-CEM defines different discovery protocols to reliably detect co-located networks. Moreover, it addresses the selection of multiple CTC nodes as a set cover problem, and includes an optimization technique based on dynamic programming to balance the energy consumption in the whole network. Extensive simulations show that CTC-CEM effectively distributes the energy consumption in the network, increasing fairness by 97% after optimization. Furthermore, the latency in establishing a channel with CTC-CEM is two orders of magnitude lower than that for device discovery in duty-cycled networks.

Despite the planned operation of enterprise wireless local area networks (WLANs), they still experience unsatisfactory performance due to several inefficiencies. One of the major issues is the so-called sticky user problem, in which users remain connected to an access point (AP) until the signal quality becomes too weak. In this paper, we leverage software-defined networking (SDN) to propose a user association solution for WLANs aiming to mitigate such inefficiencies, thus improving resource utilization. As it is a computationally hard problem, we also design various low-complexity user-AP association schemes that consider not only signal quality but also AP loads and minimum quality requirements for user traffic. Moreover, to provide simultaneous content distribution in a sustainable mode, we propose exploiting link-layer multicasting to decide on user-AP associations. Our analysis via simulations and experimentation on an open-source testbed shows that considering user-AP association jointly with multicast delivery leads to a significant performance increase over the default client-driven approach: the median throughput is 11× higher when all users request the same content and the achieved improvement decreases to 68% for 100 contents. Moreover, due to more efficient use of the airtime, unicast users achieve higher throughput if multicast delivery is exploited.

The sharing economy has made great inroads with services like Uber or Airbnb enabling people to share their unused resources with those needing them. The computing world, however, despite its abundance of excess computational resources has remained largely unaffected by this trend, save for few examples like SETI@home. We present DeCloud, a decentralized market framework bringing the sharing economy to on-demand computing where the offering of pay-as-you-go services will not be limited to large companies, but ad hoc clouds can be spontaneously formed on the edge of the network. We design incentive compatible double auction mechanism targeted specifically for distributed ledger trust model instead of relying on third-party auctioneer. DeCloud incorporates innovative matching heuristic capable of coping with the level of heterogeneity inherent for large-scale open systems. Evaluating DeCloud on Google cluster-usage data, we demonstrate that the system has a near-optimal performance from an economic point of view, additionally enhanced by the flexibility of matching.

Although communication networks have been constantly evolving and increasing their capacity with state-of-the-art solutions, it is still hard to provide reliable and high capacity communications in some cases referred to as challenged networks. In these networks, ensuring performance guarantees is hard either due to the lack of infrastructure or its limitations, such as in public safety networks, or due to the challenging communication medium, such as in mmWave networks. Challenged networks face now new constraints with the proliferation of services and applications, increasing number of connected devices, the high computing and control demands, unpredicted human behavior, and only partially-available information. CHANTS'19 aims at bringing researchers together to have a platform for discussing the challenges emerging with new use cases and real-life applications, such as edge computing or autonomous driving, and corresponding innovative approaches in tackling the limitations of the challenged networks. Moreover, this workshop aims at providing another perspective to the broader audience by listing a subset of problems that communication networks, despite the advances on many fronts, still have to tackle. The expected outcomes of CHANTS'19 have the potential to influence industrial thinking about the technologies of next-generation challenged networks.

The Internet is largely a self-organizing system that adapts to changes in its operating environment. In this work, we extend these principles to service infrastructure and introduce ICON, standing for intelligent container. Technically, ICON is a container encapsulating a service that is consumed either directly by end-clients or other services. The novelty of ICON is in the ability of containers to adapt to their environment, targeting near-optimal service delivery and requiring only high-level guidance from the application management. Once deployed, containers form an overlay, observe their setting, and migrate or replicate themselves as needed, to the locations e.g., closest to service consumers. ICON captures our long-term vision for self-organizing service overlays that have the potential for global outreach. Bringing intelligence and adaptation to the level of individual containers renders a decentralized solution that has desirable properties, such as scalability, resilience, reliability, and adaptability to volatile environments. We hope that technology like ICON can open the way for more democratized service provisioning, disintermediating service providers from centralized brokers and optimizing orchestrators.

Smart antennas can unlock the potential of unlicensed spectrum by letting the coexisting networks transmit concurrently without harmful interference. This is possible by strategically allocating the antenna degrees-of-freedom for both beamforming toward the intended receiver and interference nulling toward the victim receiver(s). Our solution, named Xzero, achieves this goal for the particular case of LTE-unlicensed (LTE-U) and WiFi by overcoming the challenges of cross-technology interference nulling by a null search at the LTE-U BS with assistance from the WiFi network. Our demo shows a running prototype of Xzero implemented using USRP SDR platform running srsLTE and commodity WiFi hardware. We illustrate the change in the airtime of colocated WiFi and LTE-U networks upon activation of Xzero and fast reconfiguration of the null beam upon a change in WiFi node's location.

To avoid the foreseeable spectrum crunch, LTE operators have started to explore the option to directly use the underutilized unlicensed spectrum in 5 GHz UNII bands being mainly used by IEEE 802.11 (WiFi). However, as LTE is not designed with shared spectrum access in mind, it has a potential to seriously harm Wi-Fi, Currently suggested solutions focus on forcing LTE-U to introduce coexistence gaps in either frequency, time, or space domain, and are addressing the coexistence only indirectly due to the lack of coordination among the coexisting WiFi and LTE-U networks. Contrary to these schemes, our proposal introduces explicit cooperation between neighboring LTE-U and WiFi networks. We suggest that LTE-U BSs equipped with multiple antennas can create coexistence gaps in space domain in addition to the time domain gaps by means of cross-technology interference nulling towards WiFi nodes in the interference range. In return, LTE-U can increase its own airtime utilization while trading off slightly its antenna diversity. We demonstrate that such cooperation offers benefits to both WiFi and LTE-U in terms of improved throughput and decreased channel access delay. More specifically, system-level simulations reveal a throughput gain up to 221% for LTE-U network and up to 44% for WiFi network depending on the setting, e.g., distance between the two cells, number of LTE antennas, and WiFi nodes in the LTE-U BS neighborhood. Our approach provides significant benefits especially for moderate separation distances between LTE-U/WiFi cells where interference from a neighboring network might be severe due to the hidden network problem.

Scoped-flooding is used for content discovery in a broad networking context and it has significant impact on the design of caching algorithms in a communication network. Despite its wide usage, a thorough analysis on how scoped-flooding affects a network's performance, e.g., caching and content discovery efficiency, is missing. To develop a better understanding, we first model the behavior of scoped-flooding by the help of a theoretical model on network growth and utility. Next, we investigate the effects of scoped-flooding on various topologies in information-centric networks (ICNs). Using the proposed ring model, we show that flooding can be constrained within a small neighborhood to achieve most of the gains which come from areas with relatively low growth rate, i.e., the network edge. We also study two flooding strategies and compare their behaviors. Given that caching schemes favor more popular items in competition for cache space, popular items are expected to be stored in diverse parts of the network compared to the less popular items. We propose to exploit the resulting divergence in availability along with the routers' topological properties to fine tune the flooding radius. Our results shed light on designing both efficient content discovery mechanism and effective caching algorithms for future ICN.

Efficient resource allocation in enterprise wireless local area networks(WLAN) has become more paramount with the shift of traffic toward WLANs and increasing share of the video traffic. Unfortunately, current practise of client-driven association to APs has several shortcomings, e.g., sticky client problem. As a remedy, we propose to move the AP association decision to a periodically-running central controller which aims to maximize the proportionally-fair network throughput. After formulating the optimal mapping problem, we devise several heuristics requiring various degrees of knowledge, e.g., pairwise user-AP link rates, throughput demand of each user. Our analysis via simulations on realistic scenarios (conference, office, and shopping mall) shows the superior performance of our proposals in terms of aggregate logarithmic throughput. While the utility gain over the conventional client-driven approach is modest, up to 18%, the resulting increase in the weakest user's throughput is significant (71-120%) as well as that of AP load balance and fairness of user throughputs. Moreover, our evaluations reveal a very small optimality gap (between 0.1-5%). The highest gain is observed in the conference setting where the users are unevenly distributed in the network and hence there is a huge load imbalance among the APs. While schemes requiring more knowledge, i.e., on handover-cost and traffic demands, perform the best, a naive approach which runs periodically and assigns each user to the AP providing the highest signal level to that user maintains up to 41% gain in the weakest user's throughput over the client-driven handover approach.

Future wireless networks face a great challenge in spectral resource management to meet an overwhelming demand for network capacity. In 5G systems, Device-to-Device (D2D) communications is a key technology to alleviate this “capacity crunch” while content consumption is the key usage mode. In this work, we devise a resource allocation problem for a cellular network that facilitates the delivery of requested contents to its users via either BS mode or D2D mode. By solving the formulated optimization problem, we investigate the interplay between D2D transmissions, cache characteristics, and mode selection preference. Our numerical results suggest that while enabling D2D operation improves delivery performance significantly, how much a network can deliver in D2D mode is determined by both the network density and cache capacity.

With growing data demand and the current dearth of spectrum, mobile operators are looking for new frequency bands to satisfy data-hungry users. One promising avenue of expansion is TV white spaces, which are currently available to secondary users as long as they do not interfere with primary (i.e., incumbent) users. In this work, we explore the benefits of offloading cellular traffic onto TV white spaces. We develop an analytical model and efficient algorithms to assign users to the cellular network or white space channels by considering their channel gains, multi-user interference on white space channels, and the cost of switching between different networks. We perform extensive data-driven simulations in two representative urban scenarios based on publicly available datasets. Our results show that white spaces can increase capacity by 16-62%, depending on the environment, but careful network selection is necessary to ensure that maximum capacity gains are realized. Moreover, we show that white spaces provide a significant benefit in serving indoor users where cellular channel conditions are poor. Specifically, our algorithms can offload up to 40% of cellular traffic to white spaces for indoor scenarios.

Hop count limitation helps controlling the spread of messages as well as the protocol complexity and overhead in a distributed network. For a mobile opportunistic network, we examine how the paths between any two nodes change with increasing number of hops a message can follow. Using the all hops optimal path (AHOP) problem, we represent the total delay of a route from a source node to a destination node as additive weight and use the number of encounters as a representation of bottleneck weight. First, we construct a static (contact) graph from the meetings recorded in a human contact trace and then analyze the change in these two weights with increasing hop count. Alternatively, we aggregate all the contact events in a time interval and construct several time-aggregated graphs over which we calculate the capacity metrics. Although, we observe differences in the properties of the static and the time-aggregated graphs (e.g., higher connectivity and average degree in static graph), our analysis shows that second hop brings most of the benefits of multi-hop routing for the studied networks. However, the optimal paths —path that provides the most desirable bottleneck/additive weight— are achieved at further hops, e.g, hop count ≈ 4. Our finding, which is also verified by simulations, is paramount as it puts an upper bound on the hop count for the hop-limited routing schemes by discovering the optimal hop count for both additive and bottleneck weights.

While there is a drastic shift from host-centric networking to content-centric networking, how to locate and retrieve the relevant content efficiently, especially in a mobile network, is still an open question. Mobile devices host increasing volume of data which could be shared with the nearby nodes in a multi-hop fashion. However, searching for content in this resource-restricted setting is not trivial due to the lack of a content index, as well as, desire for keeping the search cost low. In this paper, we analyze a lightweight search scheme, hop-limited search, that forwards the search messages only till a maximum number of hops, and requires no prior knowledge about the network. We highlight the effect of the hop limit on both search performance (i.e., success ratio and delay) and associated cost along with the interplay between content availability, tolerated waiting time, network density, and mobility. Our analysis, using the real mobility traces, as well as synthetic models, shows that the most substantial benefit is achieved at the first few hops and that after several hops the extra gain diminishes as a function of content availability and tolerated delay. We also observe that the return path taken by a response is on average longer than the forward path of the query and that the search cost increases only marginally after several hops due to the small network diameter.

Searching content in mobile opportunistic networks is a difficult problem due to the dynamically changing topology and intermittent connections. Moreover, due to the lack of global view of the network, it is arduous to determine whether the best response is discovered or search should be spread to other nodes. A node that has received a search query has to take two decisions: (i) whether to continue the search further or stop it at the current node (current search depth) and, independently of that, (ii) whether to send a response back or not. As each transmission and extra hop costs in terms of energy, bandwidth and time, a balance between the expected value of the response and the costs incurred must be sought. In order to better understand this inherent trade-off, we assume a simplified setting where both the query and response follow the same path. We formulate the problem of optimal search for the following two cases: a node holds (i) exactly matching content with some probability, and (ii) some content partially matching the query. We design static search in which the search depth is set at query initiation, dynamic search in which search depth is determined locally during query forwarding, and learning dynamic search which leverages the observations to estimate suitability of content for the query. Additionally, we show how unreliable response paths affect the optimal search depth and the corresponding search performance. Finally, we investigate the principal factors affecting the optimal search strategy.

In this paper, we formulate the energy-efficient cooperative sensing scheduling scheme for a cognitive radio network (CRN) with heterogeneous primary signal-to-noise ratio at each secondary user (SU). In the considered CRN, cognitive base station assigns a set of SUs for each frequency with the aim of minimizing the total energy consumption for sensing while meeting the asserted probability of detection and false alarm requirements by employing cooperation. Sensing duration for a target detection performance increases with degrading channel quality. Thus, an SU with better channel conditions consumes lower energy for sensing. Additionally, an SU also spends energy to switch to the next frequency in its sensing sequence. Our scheduling scheme discovers the appropriate set of SUs for each frequency by considering the sensing and channel switching energy as well as the energy consumed for reporting the sensing outcomes. We also present a polynomial time heuristic, Energy Aware Spectrum sEnsing (EASE) that performs close to the optimal solution.

Caching is a key component of information-centric networking, but most of the work in the area focuses on simple en-route caching with limited cooperation between the caches. In this paper we model cache cooperation under a game theoretical framework and show how cache cooperation policy can allow the system to converge to a Pareto optimal configuration. Our work shows how cooperation impacts network caching performance and how it takes advantage of the structural properties of the underlying network.

We consider the problem of distributed channel selection in cognitive radio (CR) ad hoc networks (CRAHNs) in which CRs have the capability of estimating the primary channel traffic activities. We propose a distributed channel selection scheme referred to as best-fit channel selection (BFC). In BFC each CR selects a channel among the primary user (PU) channels for transmission that best fits to its transmission requirement as opposed to the widely known longest idle time channel selection (LITC) scheme, in which a CR selects the channel that has the longest expected idle time independent of its transmission needs. In a multi-user network LITC may degrade the network performance compared to the BFC. In contrast to the BFC, referred to as non-selfish, LITC is considered selfish since each CR aims to maximize its own benefit and wastes resources that may be utilized by other CRs in the network. Through a set of simulations, we highlight the performance improvement by the BFC scheme over the conventional LITC scheme. Simulation results show that the performance of BFC is significantly superior to that of the LTIC in terms of CRAHN throughput and probability of successful transmission of CRs.

The main focus of this thesis is to design and develop cognitive MAC protocols for various networks. Furthermore, we aim to explore the design tools, approaches and auxiliary systems that can be adapted to and applied in cognitive radio (CR) domain. In particular, we proposed a distributed spectrum access scheme for ad hoc CR networks (CRN) in which each secondary user (SU) acts in an altruistic way. The proposed access scheme improves CRN throughput compared to the selfish scheme that is mostly considered in the literature. Next, we propose a symbiotic operation of CRs and cognitive femtocells, in which SUs use the sensing outcome of the femto-BS and are charged accordingly. Addition of "cognitive" sensing to the femto-BS contributes to the discovery of spectrum holes and facilitates higher bandwidth at the femtocells compared to the conventional femto-BS. Currently, we focus on design and development of adaptive medium access control (MAC) protocol that considers various aspects of the operating environment (e.g. PU mobility) and the CR itself.