Large Language Models (LLMs) based on autoregressive, decoder-only Transformers generate text one token at a time, where a token represents a discrete unit of text. As each newly produced token is appended to the partial output sequence, the length grows and so does the memory and compute load, due to the expanding key-value (K/V) caches-which store intermediate representations of all previously generated tokens-in the multi-head attention (MHA) layer. As this iterative process steadily increases memory and compute demands, layer-based partitioning in resource-constrained edge environments often results in memory overload or high inference latency. To address this, aiming to reduce inference latency, we propose a resource-aware Transformer architecture partitioning algorithm, where the partitioning decision is updated at regular intervals during token generation. The approach is a myopic algorithm in the sense that it is based on instantaneously available information about device resources availability and network link bandwidths. When the algorithm is first executed, it generates a placement of blocks on devices, and in each consecutive time it is executed, it migrates these blocks among devices so that the sum of migration delay and inference delay remains low. Our approach partitions the decoder at the attention head-level, co-locating each attention head with its K/V cache and allowing dynamic migrations whenever resources become tight. By allocating different attention heads to different devices, we exploit parallel execution of attention heads and thus allow for substantial reductions in inference delays. Our experiments show that in small-scale settings (3-5 devices), the proposed method achieves within 15-20% of an exact optimal solver's latency, while in larger-scale tests it achieves notable improvements in inference speed and memory usage compared to state-of-the-art layer-based partitioning approaches.

As edge devices become more capable and pervasive in wireless networks, there is growing interest in leveraging their collective compute power for distributed learning. However, optimizing learning at the network edge entails unique challenges, particularly when moving beyond conventional settings and objectives. While Federated Learning (FL) has emerged as a key paradigm for distributed model training, critical challenges persist. First, existing approaches often overlook the trade-off between predictive accuracy and interpretability. Second, they struggle to integrate inherently explainable models such as decision trees because their non-differentiable structure makes them not amenable to backpropagation-based training algorithms. Lastly, they lack meaningful mechanisms for continual Machine Learning (ML) model adaptation through Continual Learning (CL) in resource-limited environments. In this paper, we pave the way for a set of novel optimization problems that emerge in distributed learning at the network edge with wirelessly interconnected edge devices, and we identify key challenges and future directions. Specifically, we discuss how Multi-objective optimization (MOO) can be used to address the trade-off between predictive accuracy and explainability when using complex predictive models. Next, we discuss the implications of integrating inherently explainable tree-based models into distributed learning settings. Finally, we investigate how CL strategies can be effectively combined with FL to support adaptive, lifelong learning when limited-size buffers are used to store past data for retraining. Our approach offers a cohesive set of tools for designing privacy-preserving, adaptive, and trustworthy ML solutions tailored to the demands of edge computing and intelligent services.

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

Partitioning Artificial Intelligence (AI) models such as Deep Neural Networks (DNNs) or Transformer-based Architectures is essential for minimizing latency in resource-constrained edge computing environments, which is critical for applications such as real-time video analytics, autonomous vehicles, smart IoT systems, and most importantly, on-device Large Language Models (LLMs)1. This paper presents a demonstration of an experimental platform showcased for DNN partitioning for the inference stage, which comprises a WiFi network of Raspberry Pi 4 devices. The platform supports research on optimizing inference in distributed setups, focusing on performance and resource usage and it can support several architectures, ranging from simple and deep NNs, to more advanced transformer-based architectures.

We study the problem of online learning of optimal offloading policies for image processing tasks, for minimizing a cost that is weighted sum of transmit energy and object recognition error rate. A mobile node generates image processing tasks that involve object recognition. There exist three options: (i) transmit the image to a remote server for processing with a deep-learning (DL) model, (ii) process locally with a simpler model, (iii) apply a lightweight, error-prone technique for object detection, and if objects are detected, then send image to the server. The proper offloading decision requires knowledge of the transmit energy cost and object recognition error rate for each option. However, these processes are non-stationary due to unpredictable object occurrence, mobility and propagation dynamics, and they depend on the object inference result which is unknown at decision time. We cast the problem as an adversarial multi-armed bandit, in which the EXP3 algorithm achieves sublinear regret. For the constrained problem, we propose an algorithm that extends EXP3 and achieves good regret in the objective and constraint, thus asymptotically learning the optimal static ran-domized offloading policy, while satisfying the error constraint. Performance is validated via numerical experiments informed by real-life object recognition measurements and models.

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

Emerging energy management systems (EMS) involve devices and services provided by multiple stakeholders. In order to improve the interoperability of these systems, state of the art efforts propose an interoperability middleware that mediates the communication between end-user applications and EMS components. The potential lack of trust between the different stakeholders raises the need for fine-grained access control mechanisms. However, extending the middleware to support access control in a secure and usable way is a challenging problem. In this paper, we present a solution that achieves fine-grained authorization using Verifiable Credentials (VCs). Our solution leverages VC properties to enable end-users to combine authorizations issued by different entities. Additionally, our solution integrates a cloud-based VC wallet that hides the authorization process from end-user applications, thus facilitating interoperability among EMSes and the development of new, secure applications.

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

There is a growing research interest in Federated Learning (FL), a promising approach for data privacy preservation and proximity of training to the network edge, where data is generated. Resource consumption for Machine Learning (ML) training and inference is important for edge nodes, but most of the proposed protocols and algorithms for FL are evaluated by simulations. In this demo paper, we present an environment based on distributed mini-PCs to enable experimental study of FL protocols and algorithms. We have installed low-capacity mini-PCs within a wireless city-level mesh network and deployed container-based FL components on these nodes. We show the deployed FL clients and server at different nodes in the city and demonstrate how an FL experiment can be set and run in a real environment.

This work contributes towards optimizing edge analytics in Mobile Edge Computing (MEC) systems. We consider requests for computing tasks that are generated from users and can be satisfied either locally at their devices, or they can be offloaded to an edge server in their proximity for remote execution. We study a multi-user MEC system with limited energy autonomy for the mobile devices and with limitations on the computing capability of both mobile devices and at an edge server, where users can offload part of their computation load. We define a utility over “resource residuals”, that capture the difference between the resources assigned through our decisions, and those needed in practice, and we aim at the minimization of regret, i.e., of the difference between the utility obtained by an optimal offline benchmark that knows the system evolution in hindsight, and our online decision policy. We design an algorithm that jointly learns policies for offloading computations and scheduling them for execution at the shared MEC server. We prove that our algorithm is asymptotically optimal, i.e., it has no regret over the optimal static offline benchmark, and that its performance is independent of the number of devices in the system. From our numerical evaluation we conclude that our algorithm adapts to unpredictable demand changes, it learns to identify resource-limited devices, and it learns to share the server’s resources.

Network slicing allows Mobile Network Operators (MNOs) to partition their physical infrastructure into multiple virtual logical networks, enabling the simultaneous servicing of applications with diverse Quality of Service characteristics. In this paper, we introduce and evaluate economic models and policies for the provisioning, across multiple MNOs, of network slice services to Application Providers. We introduce a Network-Slice-as-a-Service model that maps the service offered by a network slice to requirements on virtualized resources. The placement of virtualized resources over the physical infrastructure of MNOs is determined by an embedding problem, formulated as a Mixed Integer Program. We investigate the embedding under: (i) centralized approaches, where a central Broker determines the embedding for all network slice requests, and (ii) peer-to-peer approaches, where each MNO determines the embedding for the sub-set of network slice requests coming from its own customers. We introduce policies for cooperative modes, with the objective of total profit maximization, and for “coopetitive” (cooperative competition) modes, where MNOs aim to maximize their individual profits. The numerical results reveal that MNOs can maximize their aggregate and individual profits under any approach or mode, if they comply with the proposed policies.

We formulate and study the problem of online learning of the optimal controller selection policy in mobile Software-Defined Networks, where the controller-switch round-trip-time (RTT) delays are unknown and time-varying. Static optimization approaches are not helpful, since delays vary significantly (and sometimes, arbitrarily) from one slot to another, and only RTT delays from the current active controller can be easily measured. First, we model the sequence of RTT delays across time as a stationary random process so that the value at each time slot is a sample from an unknown probability distribution with unknown mean. This approach is applicable in relatively static network settings, where stationarity can be assumed. We cast the problem as a stochastic multiarmed bandit, where the arms are the different controller choices, and we fit different bandit algorithms to that setting, such as: the Lowest Confidence Bound (LCB) algorithm by modifying the known Upper Confidence Bound (UCB) one, the LCB-tuned one, and the Boltzmann exploration one. The first two are known to achieve sublinear regret, while the last one turns out to be very efficient. In a second approach, the random process of RTTs is non-stationary and thus cannot be characterized statistically. This scenario is applicable in cases of arbitrary mobility and other dynamics that affect RTT delays in an unpredictable, adversarial manner. We pose the problem as an adversarial bandit that can be solved with the EXP3 algorithm which achieves sublinear regret. We argue that all approaches can be implemented in an SDN environment with lightweight messaging. We also compare the performance of these algorithms for different problem settings and hyper-parameters that reflect the efficiency of the learning process. Numerical evaluation shows that Boltzmann exploration achieves the best performance.

In this paper, we propose an online learning approach, utilizing the framework of Online Convex Optimization (OCO) to tackle the problem of learning the optimal energy supply plan, in terms of total incurred cost, from the perspective of an electricity retailer that also owns power generators and renewable energy sources. The retailer does not have prior knowledge about key dynamic processes that affect the problem, such as future demand, renewable energy supply, and wholesale market prices. The retailer sequentially learns how to adjust the power generation plan so as to minimize the power generation cost plus the cost of buying additional power from the wholesale market, in case the planned generated amount is not enough to cover the demand. We use Online Mirror Descent (OMD) and Online Gradient Descent (OGD), and we verify that they both achieve sublinear static and dynamic regret, which compare the cumulative cost of each algorithm against that of the optimal offline static and dynamic solution respectively. In particular, dynamic regret appears to be well aligned with the considered setting since it captures the fact that a power generator can dynamically change its power output between consecutive time slots based on only small adjustments and not in an arbitrary fashion, due to ramp constraints. Our model can capture different settings of electricity markets. Simulations with real data verify that OMD precisely learns the optimal dynamic supply policy for small power adjustments between consecutive time slots.

We learn optimal user association policies for traffic from different locations to Access Points(APs), in the presence of unknown dynamic traffic demand. We aim at minimizing a broad family of α-fair cost functions that express various objectives in load assignment in the wireless downlink, such as total load or total delay minimization. Finding an optimal user association policy in dynamic environments is challenging because traffic demand fluctuations over time are non-stationary and difficult to characterize statistically, which obstructs the computation of cost-efficient associations. Assuming arbitrary traffic patterns over time, we formulate the problem of online learning of optimal user association policies using the Online Convex Optimization (OCO) framework. We introduce a periodic benchmark for OCO problems that generalizes state-of-the-art benchmarks. We exploit inherent properties of the online user association problem and propose PerOnE, a simple online learning scheme that dynamically adapts the association policy to arbitrary traffic demand variations. We compare PerOnE against our periodic benchmark and prove that it enjoys the no-regret property, with additional sublinear dependence of the network size. To the best of our knowledge, this is the first work that introduces a periodic benchmark for OCO problems and a no-regret algorithm for the online user association problem. Our theoretical findings are validated through results on a real-trace dataset.

We study physical-layer (PHY) baseband functional split policies in 5G Centralized Radio-Access-Network (C-RAN) architectures that include a central location, the baseband unit (BBU) with some BBU servers, and a set of Base Stations (BSs), the remote radio heads (RRHs), each with a RRH server. Each RRH is connected to the BBU location through a fronthaul link. We consider a scenario with many frame streams at the BBU location, where each stream needs to be processed by a BBU server before being sent to a remote radio-head (RRH). For each stream, a functional split needs to be selected, which provides a way of partitioning the computational load of the baseband processing chain for stream frames between the BBU and RRH servers. For streams that are served by the same BBU server, a scheduling policy is also needed. We formulate and solve the joint resource allocation problem of functional split selection, BBU server allocation and server scheduling, with the goal to minimize total average end-to-end delay or to minimize maximum average delay over RRH streams. The total average end-to-end delay is the sum of (i) scheduling (queueing) and processing delay at the BBU servers, (ii) data transport delay at the fronthaul link, and (iii) processing delay at the RRH server. Numerical results show the resulting delay improvements, if we incorporate functional split selection in resource allocation.

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

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

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

Panoramic, or 360° video streaming and playback are prime components of mixed-reality and panoramic movie-viewing experiences, they offer an immersive viewing experience, and they are becoming increasingly popular, albeit very resource-demanding. Caching of 360° video content at caches close to the user reduces content delivery delay and bandwidth consumption. In 360° video streaming, the recently proposed tiling approach allows the streaming of different parts (tiles) of the 360° content at different resolutions as opposed to monolithic single-stream transmission.

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

Distributed caching architectures have been proposed for bringing content close to requesters, and the key problem is to design caching algorithms for reducing content delivery delay, which determines to an extent the user Quality of Experience (QoE). This problem obtains an interesting new twist with the advent of advanced layered-video encoding techniques such as Scalable Video Coding. In this paper, we show that the problem of finding the caching configuration of video encoding layers that minimizes delivery delay for a network operator is NP-Hard, and we establish a pseudopolynomial-time optimal solution by using a connection with the multiple-choice knapsack problem. Next, we design caching algorithms for multiple network operators that cooperate by pooling together their co-located caches, in an effort to aid each other, so as to avoid large delays due to fetching content from distant servers. We derive an approximate solution to this cooperative caching problem by using a technique that partitions the cache capacity into amounts dedicated to own and other operators' caching needs. Trace-driven evaluations demonstrate up to 25 percent reduction in delay over existing caching schemes. As a side benefit, our algorithms achieve smoother playback for video streaming applications, with fewer playback stalls and higher decoded quality.

Sharing economy is a game-changing business paradigm that is currently permeating several industrial sectors. This paper aims to build a fundamental theory of the sharing economy of the computational capacity resource of Cloud Service Providers (CSPs). CSPs aim to cost-efficient serve geographically dispersed customers that often request computational resource-demanding services. The formation of CSP federations arises as an effective means to manage these diverse and time-varying service requests. In this paper, we introduce innovative federation models and policies for profitable federations that also achieve adequate QoS for their customers. Taking in account the flexible cloud computing service model, we abstract the virtualized infrastructure of each CSP to an M/M/1 queueing system, we formulate the CSP revenue and cost functions, and we study the task forwarding-based (TF) and the capacity sharing-based (CS) federation approaches. Under TF, each CSP may forward part of its workload to other federated CSPs, while under CS each CSP may share parts of its computational infrastructure with others. For both approaches, we propose two operation modes with different degree of CSPs' cooperation: (i) the joint business mode, where the CSPs fully cooperate: they jointly decide on the federation policies that maximize the total federation profit which is shared fairly among them; (ii) the reward-driven mode, where selfinterested CSPs participate in a game: they adjust their responses to federation policies aiming to maximize their individual profits. The results reveal that our policies lead to effective federations, which are beneficial both for CSPs and for customers.

Content providers (CPs) increasingly deploy network infrastructures that oftentimes reach up to the wireless network edge, i.e. base stations or small cells. Hence, they are interested in optimizing resource allocation and relevant performance metrics for that infrastructure. On the other hand, mobile apps featuring streaming content (e.g. video, music) come with social-networking and content-sharing capabilities among users. These need to be taken into account in resource allocation since they decisively shape content demand. In this work, we introduce mathematical optimization problems about resource allocation at the wireless network edge, which obtain interesting twists when social-network diffusion is considered. Specifically, we consider, (i) the problem of content caching and user targeting through the recommender system of the app, with the goal to maximize the social diffusion effect of cached content, and (ii) the problem of user targeting through the mobile app recommender system, so that the available wireless bandwidth is utilized as efficiently as possible.

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

Mobile location-based advertising has seen a lot of progress recently. We study the problem of optimal user targeting and monetization through advertising, from the point of view of the owner of a venue such as a shopping mall, an urban shopping district or an airport. The fundamental distinguishing characteristic of advertising in this setup is that the probability that the user will respond to an ad depends on timeliness of ad projection, hence it is important to target a mobile user with an appropriate ad or offer at the right time. A set of mobile users roam around the venue. Each user is profiled in terms of preferences based on prior visits. The system knows estimated instantaneous locations of users in the venue, e.g. through WiFi access point connectivity. A machine-learning model is used to derive a per-user time-varying probability of response to an ad, which depends on the relevance of the ad (store) to the user profile and on the time-varying physical proximity of the user to the store. Each store has a set of available ads, and each time the user responds to a projected ad, an amount is paid by the store to the venue owner. We use a stochastic-optimization framework based on Lyapunov optimization to address the problem of advertisement selection and allocation for maximizing the long-term average revenue of the venue owner subject to: (i) a constraint on maximum average ad projection rate per user for preventing user saturation, and (ii) a long-term average budget constraint for each store. We derive an algorithm that operates on a time slot basis by solving a simple assignment problem with instantaneous user locations while being agnostic to user mobility statistics. We test our algorithm with a real dataset of check-ins from Foursquare, complemented with data from user questionnaires. Our approach results in substantial improvement in revenue compared to approaches that are location- or relevance-agnostic.

Recent technological advances have rendered storage a readily available resource, yet there exist few examples that use it for enhancing network performance. We revisit in-network storage and we evaluate its usage as an additional degree of freedom in network optimization. We consider the network design problem of maximizing the volume of end-to-end transferred data and we derive storage allocation (placement) solutions. We show that different storage placements have different impact on the performance of the network and we introduce a systematic methodology for the derivation of the optimal one. Accordingly, we provide a framework for the joint optimization of routing and storage control (usage) in dynamic networks for the case of a single commodity transfer. The derived policies are based on time-expanded graphs and ensure maximum performance improvement with minimum possible storage usage. We also study the respective multiple commodity problem, where the network link capacities and node storage resources are shared by the different commodities. A key advantage of our methodology is that it employs algorithms that are applicable to both centralized as well as to distributed execution in an asynchronous fashion, and thus, no tight synchronization is required among the various involved storage and routing devices in an operational network. We also present an extensive performance evaluation study using the backbone topology and actual traffic traces from a large European Internet Service Provider, and a number of synthetic network topologies. Our results show that indeed our approach offers significant improvements in terms of delivery time and transferred traffic volume.

We study network response to a stream of queries that require computations on remotely located data, and we seek to characterize the network performance limits in terms of maximum sustainable query rate that can be satisfied. The available network setup consists of (i) a communication network graph with finite-bandwidth links over which data is routed, (ii) computation nodes with certain computation capacity, over which computation load is balanced, and (iii) network nodes that need to schedule raw and processed data transmissions. Our aim is to design a universal methodology and distributed algorithm to adaptively allocate resources in order to support maximum query rate. The proposed algorithms extend in a nontrivial way the backpressure (BP) algorithm to take into account computations carried out in the presence of query streams. They contribute to the fundamental understanding of network computation performance limits when the query rate is limited by both the communication bandwidth and the computation capacity, a classical setting that arises in streaming big data applications in network clouds and fogs.

Distributed caching architectures have been proposed for bringing content close to requesters and the key problem is to design caching algorithms for reducing content delivery delay. The problem obtains an interesting new twist with the advent of advanced layered-video encoding techniques such as Scalable Video Coding (SVC). We show that the problem of finding the caching configuration of video encoding layers that minimizes average delay for a network operator is NP-Hard, and we establish a pseudopolynomial-time optimal solution using a connection with the multiple-choice knapsack problem. We also design caching algorithms for multiple operators that cooperate by pooling together their co-located caches, in an effort to aid each other, so as to avoid large delays due to downloading content from distant servers. We derive an approximate solution to this cooperative caching problem using a technique that partitions the cache capacity into amounts dedicated to own and others' caching needs. Numerical results based on real traces of SVC-encoded videos demonstrate up to 25% reduction in delay over existing (layer-agnostic) caching schemes, with increasing gains as the video popularity distribution gets steeper, and cache capacity increases.

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

A web crawler is responsible for discovering and downloading new pages on the Web as well as refreshing previously downloaded pages. During these operations, the crawler issues a large number of HTTP requests to web servers. These requests increase the energy consumption and carbon footprint of the web servers since computational resources are used while serving the requests. In this work, we introduce the problem of green web crawling, where the objective is to devise a page refresh policy that minimizes the total staleness of pages in the repository of a web crawler, subject to a constraint on the amount of carbon emissions due to the processing on web servers. For the case of one web server and one crawling thread, the optimal policy turns out to be a greedy one. At each iteration, the page to be refreshed is selected based on a metric that considers the page's staleness, its size, and the greenness of the energy consumed at the web server premises. We then extend the optimal policy to the cases of 1) many servers; 2) multiple threads; and 3) pages with variable freshness requirements. We conduct simulations on a real data set that involves a large web server collection hosting around two billion pages. We present experimental results for the optimal page refresh policy as well as for various heuristics, in an effort to study the effect of different factors on performance.

The liberalization of wireless communication services markets and the subsequent competition among network operators, is expected to foster optimal utilization of the scarce wireless spectrum and ensure the provision of cost-efficient services to users. However, such markets may function inefficiently due to collusion of operators which yields a de-facto monopoly. Although it is illegal and detrimental to the users, creation of such cartels arises often in the form of implicit price fixing. In this paper, we consider a general such market where a set of operators sell communication services to a large population of users. We use an evolutionary game model to capture the user dynamics in selecting operators, under limited information about the actual service quality, and we analyze the anticipated interaction of the operators using coalitional game theory. We define a coalition formation game in order to rigorously study the conditions that render monopolistic or oligopolistic markets stable under different notions of coalition stability. We also provide direct and indirect regulation methods, such as setting price upper bounds or allocating different amounts of spectrum, in order to discourage undesirable equilibriums. Our approach provides intuitions about collusion strategies, as well as on directions for identifying and preventing them.

Recommender systems used in current online social platforms make recommendations by only considering how relevant an item is to a specific user but they ignore the fact that, thanks to mechanisms like sharing or re-posting across the underlying social network, an item recommended to a user i propagates through the network and can reach another user j without needing to be explicitly recommended to j too. Overlooking this fact may lead to an inefficient use of the limited recommendation slots. These slots can instead be exploited more profitably by avoiding unnecessary duplicates and recommending other equally relevant items.

Caching at the network edge is considered a promising solution for addressing the ever-increasing traffic demand of mobile devices. The problem of proactive content replication in hierarchical cache networks, which consist of both network edge and core network caches, is considered in this paper. This problem arises because network service providers wish to efficiently distribute content so that user-perceived performance is maximized. Nevertheless, current high-complexity replication algorithms are impractical due to the vast number of involved content items. Clustering algorithms inspired from machine learning can be leveraged to simplify content replication and reduce its complexity. Specifically, similar items could be clustered together, e.g., according to their popularity in space and time. Replication on a cluster-level is a problem of substantially smaller dimensionality, but it may result in suboptimal decisions compared to item-level replication. The factors that cause performance loss are identified and a clustering scheme that addresses the specific challenges of content replication is devised. Extensive numerical evaluations, based on realistic traffic data, demonstrate that for reasonable cluster sizes the impact on actual performance is negligible.

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

In this work, we demonstrate and validate a novel architecture for next generation cellular networks that enables collaborative forwarding at Layer 2 among adjacent eNBs with the aid of enhanced user equipment (UE) devices, that act voluntarily as packet forwarders. We introduce an evolved-UE (eUE) which is capable of operating simultaneously over multiples eNBs in order to enable reliable multi-hop operation through relaying and to achieve low-latency communication through efficient L2/MAC forwarding. For the demonstration and the evaluation of this architecture, we used the OpenAirInterface emulation platform to implement it, and also to evaluate its performance. The obtained results show that, the proposed architecture achieves significant reduction in latency (up to 16.94%) and improvement on packet loss rate (up to 59.25%), as the number of the employed eUEs increases with increasing BLER up to 20%. Moreover, the proposed architecture enables eUEs to increase the aggregated data rate in downlink by exploiting data connection to multiple eNBs.

In this paper, we propose a novel architecture for next generation cellular networks that enables collaborative forwarding at Layer 2 among adjacent eNBs with the aid of enhanced user equipment (UE) devices, that act voluntarily as packet forwarders. Therefore, legacy UEs are leveraged as active network elements being capable of operating simultaneously over multiple base stations (eNBs). To this end, we introduce an evolved-UE (eUE) in order to enable reliable multi-hop operation through relaying and to achieve low-latency communication through efficient L2/MAC forwarding. Through extensive experimentation with OpenAirInterface emulation platform, we evaluated the performance and also validated the feasibility of the proposed architecture. Our results show that, in certain use cases corresponding to public safety and moving/small cell scenarios, the proposed architecture achieves significant reduction in latency (up to 16.94%) and improvement on packet loss rate (up to 59.25%), as the number of the employed eUEs increases with increasing BLER up to 20%. Moreover, the proposed architecture enables eUEs to increase the aggregated data rate in downlink by exploiting data connection to multiple eNBs at the expense of extra power consumption, which calls for the appropriate incentives to enable such a cooperation.

In cognitive radio networks, secondary users (SUs) may cooperate with the primary user (PU) so that the success probability of PU transmissions are improved, while SUs obtain more transmission opportunities. However, SUs have limited power resources and, therefore, they have to take intelligent decisions on whether to cooperate or not and at which power level, to maximize their throughput. Cooperation policies in this framework require the solution of a constrained Markov decision problem with infinite state space. In our work, we restrict attention to the class of stationary policies that take randomized decisions of an SU activation and its transmit power in every time slot based only on spectrum sensing. Assuming infinitely backlogged SUs queues, the proposed class of policies is shown to achieve the maximum throughput for the SUs, while significantly enlarging the stability region of PU queue. The structure of the optimal policies remains the same even if the assumption of infinitely backlogged SU queues is relaxed. Furthermore, the model is extended for the case of imperfect channel sensing. Finally, a lightweight distributed protocol for the implementation of the proposed policies is presented, which is applicable to realistic scenarios.

In this work, we present the AGILE Spectrum Adaptation system that is able to dynamically tune the channel central frequency and bandwidth of wireless links in an adaptive to the interference and traffic conditions way. The developed system is able to detect under-utilised spectrum fragments and optimally adjust the occupied spectrum. Through the online execution of 3 specifically designed experimental scenarios, we demonstrate the ability to implement distributed spectrum adaptation in commercial WLAN deployments, along with the obtained performance benefits.

In this paper we evaluate an energy-aware relay selection mechanism which exploits channel state information and the availability of buffers at relays to perform flexible relaying based on a backpressure-driven optimization model. This model ensures the maximization of the cell throughput while maintains the stability of backlog queues. Performance evaluation is conducted using a System Level Simulator (SLS) which is fully compliant with IEEE 802.16m and supports various relaying scenarios. The Below Roof Top (BRT) relaying scenario is considered in this work. A holistic and flexible energy framework is implemented to capture the energy consumption of the cellular network nodes. The model maps the RF output power radiated at the antenna elements of each node including relays on the network to the total supply power of the node equipment. Two derivatives of the proposed mechanism, half-duplex and full-duplex are proposed and evaluated. Results of the two derivatives on BRT relaying scenario revealed noticeable increases for both cell throughput and system energy efficiency of the cell-edge users compared to the conventional relaying protocol and the non cooperative scheme.

In cognitive radio networks, secondary users (SUs) may cooperate with the primary user (PU) in order to obtain more transmission opportunities and thus maximize their throughput. The synergy consists in the following: the SU opts to cooperate by using its own transmit power to improve the probability of successful transmission of the PU. By increasing the probability of successful packet transmission for the PU, the SU essentially increases the service rate of the PU queue and thus, for given packet arrival rate, it increases the chances that it will be empty, and the channel will be free to use. Due to power limitations however, SUs have to take intelligent decisions on whether to cooperate or not and at which power level. Cooperation policies in this framework require the solution of a constrained Markov decision problem with infinite state space. In our work, we restrict attention to the class of stationary policies that take randomized decisions of an SU activation and its transmit power in every time slot based only on spectrum sensing. The proposed class of policies is shown to achieve the same set of SU rates as the more general policies, while significantly enlarging the stability region of the PU queue. Finally, a lightweight distributed protocol based on the proposed class of policies is presented, which is amenable to implementation in realistic scenarios.

This paper explores the problem of virtual machine (VM) allocation in a network of cloud server facilities which are deployed in different geographical areas. Each cloud server facility is connected to the conventional power grid network and in addition it is supported by an attached renewable energy source (RES). We address the problem of energy-efficient task allocation in the system in the presence of a time-varying grid energy price and the unpredictability and time variation of provisioned power by the RES. The objective is to reduce the total cost of power consumption for the operator. The key idea is to match the VM load with the RES provisioned power. Each request for a task to be executed in the cloud is associated with a VM request with certain resource requirements and a deadline by which it needs to be completed. The cloud provider has to create a VM with the resource requirements of the request and to execute the VM before the deadline. We propose an online algorithm with given look-ahead horizon, in which the grid power prices and patterns of output power of the RESs are known a priori and we compare it with a greedy online algorithm. Numerical results on real traces of cloud traffic and renewable source generation patterns are encouraging in terms of the performance of our techniques and motivate further research on the topic.

Participatory sensing has emerged as a novel paradigm for data collection and collective knowledge formation about a state or condition of interest, sometimes linked to a geographic area. In this paper, we address the problem of incentive mechanism design for data contributors for participatory sensing applications. The service provider receives service queries in an area from service requesters and initiates an auction for user participation. Upon request, each user reports its perceived cost per unit of amount of participation, which essentially maps to a requested amount of compensation for participation. The participation cost quantifies the dissatisfaction caused to user due to participation. This cost is considered to be private information for each device, as it strongly depends on various factors inherent to it, such as the energy cost for sensing, data processing and transmission to the closest point of wireless access, the residual battery level, the number of concurrent jobs at the device processor, the required bandwidth to transmit data and the related charges of the mobile network operator, or even the user discomfort due to manual effort to submit data. Hence, participants have strong motive to mis-report their cost, i.e. declare a higher cost that the actual one, so as to obtain higher payment. We seek a mechanism for user participation level determination and payment allocation which is most viable for the provider, that is, it minimizes the total cost of compensating participants, while delivering a certain quality of experience to service requesters. We cast the problem in the context of optimal reverse auction design, and we show how the different quality of submitted information by participants can be tracked by the service provider and used in the participation level and payment selection procedures. We derive a mechanism that optimally solves the problem above, and at the same time it is individually rational (i.e., it motivates users to participate) and incentive-compatible (i.e. it motivates truthful cost reporting by participants). Finally, a representative participatory sensing case study involving parameter estimation is presented, which exemplifies the incentive mechanism above.

Cognitive radio systems have gathered a lot of research interest during the last decade. Accuracy of spectrum sensing and efficiency of free spectrum utilization are considered as the primary objectives in this emerging technology, which promises a boost in wireless network performance, through exploitation of underutilized licensed frequency bands. As the focus of researchers is usually on these two major challenges, other aspects have been in part underestimated. In this work, we consider two factors that are rather important for evaluation of cognitive platforms, namely sensing delay and energy efficiency. The first is related to the latency induced by the spectrum sensing process and its impact on sensing efficiency, which is tightly connected to both the QoS performance of secondary users and the protection of primary users. On the other hand, energy consumption is considered as a crucial issue in all types of wireless communications, due to restricted battery autonomy of mobile devices, as well as for moving towards "greener" solutions in telecommunications. Therefore, it is important to extend existing testbed experimentation tools and develop new ones, in order to equip cognitive testbeds with such advanced monitoring capabilities. In this work, we present a monitoring procedure that has been directly integrated in the experimentation tools of the CREW testbed federation and demonstrate how it aids in the online evaluation of four different cognitive platforms in terms of the aforementioned metrics.

In this paper, we present an implementation design of a TDMA protocol for the canonical diamond-topology network containing a source, two relays and a destination (single unicast session). Getting inspired by the established Lyapunov-methodology, we propose an online strategy for the relay selection/scheduling problem. In contrast to existing works, we implement this strategy inside the proposed TDMA protocol in order to operate over a CSMA enabled Wi-Fi infrastructure-less network. We elaborate a network controller within the TDMA frame to solve a global optimization problem at each time slot in a centralized manner. In our formulation, we consider the class of scheduling policies that select concurrently a non-interfering subset of links. Our architecture is tailored to achieve the objectives of stabilizing the network and either maximizing throughput or minimizing the total power consumption. Our scheme has been implemented and tested thoroughly through experimentation in the NITOS wireless testbed by exploiting Wi-Fi technology features. The results revealed significant increase in networking efficiency for throughput maximization.

The design of efficient Demand Response (DR) mechanisms for the residential sector entails significant challenges, due to the large number of home users and the negligible impact of each of them on the market. In this paper, we introduce a hierarchical market model for the smart grid where a set of competing aggregators act as intermediaries between the utility operator and the home users. The operator seeks to minimize the smart grid operational cost and offers rewards to aggregators toward this goal. Profit-maximizing aggregators compete to sell DR services to the operator and provide compensation to end-users in order to modify their preferable consumption pattern. Finally, end-users seek to optimize the tradeoff between earnings received from the aggregator and discomfort from having to modify their pattern. Based on this market model, we first address the benchmark scenario from the point of view of a cost-minimizing operator that has full information about user demands. Then, we consider a DR market, where all entities are self-interested and non-cooperative. The proposed market scheme captures the diverse objectives of the involved entities and, compared to flat pricing, guarantees significant benefits for each. Using realistic demand traces, we quantify the arising DR benefits. Interestingly, users that are extremely willing to modify their consumption pattern do not derive maximum benefit.

Queue-based cross-layer optimization algorithms have recently been a subject of intensive research in wireless networks. Their purpose is to guarantee stable operation and to achieve some form of fairness among users, whenever the traffic demand exceeds network capacity. Despite the plethora of work in this field, the scenario where one or more nodes declare false queue backlog values in order to gain throughput advantage remains unexplored. In this paper we examine this type of selfish misbehavior, concentrating on a specific class of algorithms, the so-called quadratic Lyapunov-function-based algorithms (QLA). In particular, the effect of backlog misreporting on a single-hop access network with contending stations is evaluated through simulations. A simple framework for the detection of misbehaving nodes is proposed, under the assumption that the access-point is aware of the utility functions of the stations. The detection approach exploits the fact that under QLA the throughput of a node must be approximately equal to an “expected” value, derived from the reported queue backlogs.

Opportunistic scheduling exploits multiuser diversity for improving the performance of wireless systems. However, it requires instantaneous channel state information (CSI) to be available at the transmitter side. Since acquiring CSI is resource consuming, it always comes at a cost. We consider the problem of efficient channel state estimation in the context of 4G mobile broadband systems. Our proposed mechanism selects the subcarriers to be probed per user, by estimating the anticipated tentative rate with and without probing. For the latter an informed guess of the channel state is performed. The distinguishing characteristic of this work is that the channel state evolution is assumed Markovian, thus capturing the impact of mobility, contrary to the much simpler i.i.d. case hitherto considered. Based on this probing mechanism we derive a subcarrier allocation algorithm that aims at maximizing the sum rate of the system. Our simulations indicate that the proposed algorithm leads to significant performance benefits.

We study the problem of minimizing the long-term average power grid operational cost through power demand scheduling. A controller at the operator side receives consumer power demand requests with different power requirements, durations and time flexibilities for their satisfaction. Flexibility is modeled as a deadline by which a demand is to be activated. The cost is a convex function of total power consumption, which reflects the fact that each additional unit of power needed to serve demands is more expensive to provision, as demand load increases. We develop a stochastic model and introduce two online demand scheduling policies. In the first one, the Threshold Postponement (TP), the controller serves a new demand request immediately or postpones it to the end of its deadline, depending on current power consumption. In the second one, the Controlled Release (CR), a new request is activated immediately if power consumption is lower than a threshold, else it is queued. Queued demands are activated when deadlines expire or when consumption drops below the threshold. These policies admit an optimal control with switching curve threshold structure, which involves active and postponed demand. The CR policy is asymptotically optimal as deadlines increase, namely it achieves a lower bound on average cost, and the threshold depends only on active demand. Numerical results validate the benefit of our policies compared to the default one of serving demands upon arrival.

We study the impact of physical layer (PHY) transmit rate control on energy efficient estimation in wireless sensor networks. A sensor network collects measurements about an unknown evolving process. Each sensor controls its sampling rate and its PHY transmit rate to the next hop or to the Fusion Center (FC). The FC performs estimation of the unknown process based on sensor measurements and needs to adhere to an estimation accuracy constraint. The objective is to maximize sensor network lifetime. The tradeoff is that, high PHY transmit rates consume more energy per transmitted bit, but they increase the amount of transmitted sensor measurement data per unit time, and thus they aid in improving estimation quality and in satisfying the estimation error constraint. First, we study a single-hop network where sensors transmit directly to the FC. In this case, sensor sampling rates are directly mapped onto PHY transmit rates. We identify fundamental structural properties of the optimal solution, and we propose a distributed, iterative sensor PHY rate adaptation algorithm for reaching a solution, based on light-weight feedback from the FC. Next, we consider the multi-hop version of the problem, where the sensor measurement (sampling) rates, PHY transmit rates and data flows to the FC are controlled. We extend the distributed optimization framework above to include all controllable parameters, and we devise an iterative algorithm for maximizing network lifetime.

Recent technological advances have rendered storage a cheap and at large scale available resource. Yet, there exist only few examples in networking that consider storage for enhancing data transfer capabilities. In this paper we study networks with time varying link capacity and analyze the impact of node storage on their capability to convey data from source to destination. We show that storage capacity is quite beneficial in terms of the amount of data that can be pushed from the source to the destination within a given time horizon. Equivalently, storage can be used to reduce incurred delay for the delivery of a certain amount of data. For linear networks, we show that this performance improvement depends on the relative patterns of link capacity variations. We extend our study to general networks and we use a novel method that iteratively updates the minimum cut of the time expanded graph, in a constructive manner, in the sense that during the process, the storage capacity allocation in the network is shown. Next, we incorporate routing in our methodology and derive a joint storage capacity management and routing policy to maximize the amount of data transferred to the destination. This policy stems from the solution of the maximum flow problem defined for the dynamic network over a certain time period, by using the ε-relaxation solution method. The later is amenable to distributed implementation, which is a very desirable property for the large scale modern networks which operate without central control.

Extraction of patterns out of streaming data that are generated from geographically dispersed devices is a major challenge in data mining. The sequential, distributed fashion in which data become available to the decision maker, together with the fact that the decision maker needs to rely only on recently received data due to storage and communication constraints, render the objective of keeping track of data evolution a nontrivial one. We consider a set of distributed nodes that communicate directly with a central location. We address the problem of clustering distributed streaming data through a two-level clustering approach. We adopt belief propagation techniques to perform stream clustering at both levels. At the node level, a batch of data arrives at each time slot, and the goal is to maintain a set of salient data (local exemplars) at each time slot, which best represents the data received up to that slot. At each epoch, the local exemplars from distributed nodes are sent to the central location, which in turn performs a second-level clustering on them to derive a data synopsis global for the whole system. The local exemplars that emerge from the second level clustering procedure are fed back to the nodes with appropriately modified weights which reflect their importance in global clustering. As demonstrated by our experiments, the two-level belief propagation-based clustering approach together with the feedback is ideal for handling data from different nodes, as it has the same performance in terms of clustering quality with the case where the clustering is performed on the raw data sent from nodes to the central location.

Efficient association of a station with the appropriate access point has always been a challenging problem. The standard approach of considering only the Received Signal Strength, has recently been substituted by more efficient schemes that consider channel conditions, cell population etc. However, in spite of the large variety of approaches, several factors that determine to a large extent user throughput after association with an access point have been overlooked. In this work, we propose innovative metrics on which association should be based. First, we capture the contention from one-hop and interference from two-hop neighbors that is inherent in IEEE 802.11 WLAN environments. Second we include the PHY transmission rate and show preference to higher rates that reduce the above effects. Third, unlike most relevant approaches, we define an activity factor that reveals the anticipated activity due to backlogged traffic. We devise an association protocol suite, through which messages containing the information above are passed between the AP and the user to support association decisions for the uplink and downlink. We implement the proposed mechanism using the MAD-WiFi open source driver and moreover show through experiments in a wireless testbed that it significantly improves user performance in real conditions.

With the proliferation of access points and terminals, wireless access misbehavior emerges as important means of protocol misuse, whereby network entities aim at obtaining higher channel share than the one in legitimate operation. We consider the instantiation of misbehavior in the back-off mechanism in IEEE 802.11. First, we study the competition between two malicious entities that affect each other. Each entity has some private target gain in terms of access probability and attempts to misuse the protocol to its benefit so as to prolong the time until detection. We derive conditions for existence of the unique Nash Equilibrium Point (NEP). This is influenced by attacker gains and topology parameters that capture the level of contention from neighboring nodes. These quantities need to be small in order for the NEP to exist. Next, we consider simple types of cooperation between attackers that aim at alleviating the contention caused to each other.

We study the impact of physical layer (PHY) transmit rate control on energy efficient estimation in wireless sensor networks. A sensor network collects measurements and transmits them to a Fusion Center (FC) with controllable PHY transmission rates. The FC performs estimation of an unknown parameter process based on sensor measurements, and it needs to adhere to an estimation error constraint. The objective is to maximize network lifetime. High transmission rates consume more energy per transmitted bit, however they convey larger amount of data per unit time and thus can aid in satisfying the estimation error constraint. We identify basic structural properties of the optimal solution, and we propose an iterative algorithm for reaching a solution based on light-weight feedback from the FC.

Auction mechanisms are used for allocating a resource among multiple agents with the objective to maximize social welfare. What makes auctions attractive is that they are agnostic to utility functions of agents. Auctions involve a bidding method by agents-buyers, which is then mapped by a central controller to an allocation and a payment for each agent. In autonomic networks comprising self-interested nodes with different needs and utility functions, each entity possesses some resource and can engage in transactions with others to achieve its needs. In fact, efficient network operation relies on node synergy and multi-lateral resource trading. Nodes face the dilemma of devoting their limited resource to their own benefit versus acting altruistically and anticipating to be aided in the future. Wireless ad-hoc networks, peer-to-peer networks and disruption-tolerant networks are instances of autonomic networks where the challenges above arise and the traded resource is energy, bandwidth and storage space respectively. Clearly, the decentralized complex node interactions and the double node role as resource provider and consumer amidst resource constraints cannot be addressed by single-sided auctions and even more by mechanisms with a central controller. We introduce a double-sided auction market framework to address the challenges above. Each node announces one bid for buying and one for selling the resource. We prove that there exist bidding and charging strategies that maximize social welfare and we explicitly compute them. We generalize our result to a generic network objective. Nodes are induced to follow these strategies, otherwise they are isolated by the network. Furthermore, we propose a decentralized realization of the double-sided auction with lightweight network feedback. Finally, we introduce a pricing method which does not need a charging infrastructure. Simulation results verify the desirable properties of our approach.

We consider a scenario where a sophisticated jammer jams an area in which a single-channel random-access-based wireless sensor network operates. The jammer controls the probability of jamming and the transmission range in order to cause maximal damage to the network in terms of corrupted communication links. The jammer action ceases when it is detected by the network (namely by a monitoring node), and a notification message is transferred out of the jammed region. The jammer is detected by employing an optimal detection test based on the percentage of incurred collisions. On the other hand, the network defends itself by computing the channel access probability to minimize the jamming detection plus notification time. The necessary knowledge of the jammer in order to optimize its benefit consists of knowledge about the network channel access probability and the number of neighbors of the monitor node. Accordingly, the network needs to know the jamming probability of the jammer. We study the idealized case of perfect knowledge by both the jammer and the network about the strategy of each other and the case where the jammer and the network lack this knowledge. The latter is captured by formulating and solving optimization problems where the attacker and the network respond optimally to the worst-case or the average-case strategies of the other party. We also take into account potential energy constraints of the jammer and the network. We extend the problem to the case of multiple observers and adaptable jamming transmission range and propose a meaningful heuristic algorithm for an efficient jamming strategy. Our results provide valuable insights about the structure of the jamming problem and associated defense mechanisms and demonstrate the impact of knowledge as well as adoption of sophisticated strategies on achieving desirable performance.

We study the problem of maximum lifetime in wireless sensor networks that are entitled with the task of estimating an unknown parameter or process. Sensors take measurements and transfer them in multi-hop fashion to a fusion center (FC) for Maximum Likelihood (ML) estimation. To engineer the network for lifetime maximization while adhering to estimation error specifications, the number of measurements by each sensor per unit of time (namely, sensor measurement rate) and the routes to the FC are controlled. Sensor spatial correlation, measurement accuracies, link qualities and energy reserves affect sensor measurement rates and the routes to the FC, and, in turn, measurement rates and sensor characteristics impact estimation error. We show that the problem can be decomposed into separate optimization problems where each sensor autonomously takes its measurement rate and routing decisions, and we propose an iterative primal-dual algorithm with low-overhead signaling for solving it. Our work optimally captures the fundamental tradeoff between network lifetime and estimation quality and yields a solution based on distributed sensor coordination.

Jamming attacks have become prevalent during the last few years, due to the shared nature and the open access to the wireless medium. Finding the location of a jamming device is of great importance for restoring normal network operations. After detecting the malicious node we want to find its position, in order for further security actions to be taken. Our goal in this paper is the design and implementation of a simple, lightweight and generic localization algorithm. Our scheme is based on the principles of the gradient descent minimization algorithm. The key observation is that the packet delivery ratio (PDR) has lower values as we move closer to the jammer. Hence, the use of a gradient-based scheme, operating on the discrete plane of the network topology, can help locate the jamming device. The contributions of our work are the following: (a) we demonstrate, through analysis and experimentation, the way that the jamming effects propagate through the network in terms of the observed PDR. (b) we design a distributed, lightweight jammer localization system which does not require any modifications to the driver/firmware of commercial NICs. (c) We implement and evaluate our localization system on our 802.11 indoor testbed. An attractive and important feature of our system is that it does not rely on special hardware.

We consider a content sharing network of non-cooperative peers. The strategy set of each peer comprises, (i) client strategies, namely feasible request load splits to servers, and (ii) server strategies, namely scheduling disciplines on requests. First, we consider the request load splitting game for given server strategies such as First-In-First-Out or given absolute priority policies. A peer splits its request load to servers to optimize its performance objective. We consider the class of best response load splitting policies residing between the following extremes: a truly selfish, or egotistic one, where a peer optimizes its own delay, and a pseudo-selfish or altruistic one, where a peer also considers incurred delays to others. We derive conditions for Nash equilibrium points (NEPs) and discuss convergence to NEP and properties of the NEP. For both the egotistic cases, the NEP is unique. For the altruistic case, each of the multiple NEPs is an optimum, a global one for the FIFO case and a local one otherwise. Next, we include scheduling in peer strategies. With its scheduling discipline, a peer cannot directly affect its delay, but it can affect the NEP after peers play the load splitting game. The idea is that peer i should offer high priority to (and thus attract traffic from) higher-priority peers that cause large delay to i at other servers. We devise two-stage game models, where, at a first stage, a peer selects a scheduling rule in terms of a convex combination of absolute priorities, and subsequently peers play the load splitting game. In the most sophisticated rule, a peer selects a scheduling discipline that minimizes its delay at equilibrium, after peers play the load splitting game. We also suggest various heuristics for picking the scheduling discipline. Our models and results capture the dual client-server peer role and aim at quantifying the impact of selfish peer interaction on equilibria.

The impetus of the present study is to describe a downlink beamforming method that increase spectrum efficiency and significantly reduces implementation complexity and power consumption compare to beamforming technique at each sub-carrier, proposed in the ongoing IEEE 802.11n standardization. In our scheme, common transmission weight vectors are used at a set of users in all sub-carriers. Our strategy consists of simultaneously designing downlink beamformers to multiple co-channel sets of users under the constraint on providing at least a prescribed received signal-to-interference plus noise ratio (SINR) to each intended receiver. We assume that the instantaneous channel gains are known at the access point (AP) for all users. Simulation results show substantial gain for our proposed algorithms in IEEE 802.11n wireless access networks.

This paper introduces two important enhancements to the IEEE 802.11 medium access control (MAC) protocols which are not considered in the current IEEE 802.11 MAC protocol: the channel state between the transmitter and the receiver and the node queue state. Our objective is to characterize the impact of these parameters on medium access regulation, and ultimately rely on them in order to enhance the sum throughput compared to the legacy 802.11x MAC protocols. We consider the scenario of several nodes attempting to access an access point (AP). Each node is characterized by: (i) a link quality to the AP which is essentially mapped to the PHY-layer rate that can be supported; (ii) a queue length depending on the packet arrival process at that node. We make the contention window dependent on queue size (backlog) and PHY-rate. The key idea is that a node with large PHY transmission rate and large queue size should tend to use smaller contention window, so that it gets higher chances for accessing the channel. We suggest heuristic queue and link state-aware rules for defining the contention window. We demonstrate with numerical arguments a significant enlargement in the capacity region under our modified access control policies and substantial performance improvement in terms of sum throughput for this system, compared to legacy IEEE 802.11 protocols.

Peer-to-peer networks are voluntary resource sharing systems among rational agents that are resource providers and consumers. While altruistic resource sharing is necessary for efficient operation, this can only be imposed by incentive mechanisms, otherwise peers tend to behave selfishly. Selfishness in general terms means only consuming resources in order to absorb maximal utility from them and not providing resources to other peers because this would require effort and would not give any utility. In peer-to- peer networks, this behavior, known as free riding, amounts to only downloading content from others and leads to system performance degradation. In this work, we consider a reputation-based mechanism for providing incentives to peers for resource provisioning besides resource consuming. We consider networks where the access technology does not separate upstream and downstream traffic, and these flow through the same capacity-limited access link. Peers do not know other peers' strategies and their intentions to conform to the protocol and share their resources. A separate utility maximization problem is solved by each peer, where the peer allocates a portion of its link bandwidth to its own downloads, acting as client, and it also allocates the remaining bandwidth for serving requests made to it by other peers. The optimization is carried out under a constraint on the level of dissatisfaction the peer intends to cause by not fulfilling others' requests. This parameter is private information for each peer. The reputation of a peer as a server is updated based on the amount of allocated bandwidth compared to the requested one. Reputation acts towards gradually revealing hidden intentions of peers and accordingly guiding the resource allocation by rewarding or penalizing peers in subsequent bandwidth allocations. Our results confirm that the reputation mechanism discourages selfish behavior and drives the system to a state where each peer obtains utility in accordance to its hidden intentions in dissatisfying others.

Space division multiple access (SDMA) with an antenna array at the transmitter is a promising means for increasing system capacity and supporting rate-demanding services. However, the presence of an antenna array at the physical layer raises significant issues at higher layers. In this paper, we attempt to capture the impact of SDMA on access layer channel allocation, reflected on channel reuse. This impact obtains different twists in TDMA, CDMA and OFDMA due to the different nature of co-channel and cross-channel interference and the different interaction of user spatial channel characteristics with system channels, namely time slots, codes and subcarriers. We consider these access schemes in a generalized unified framework and propose heuristic algorithms for channel allocation, downlink beamforming and transmit power control so as to increase total provisioned system rate and provide QoS to users in the form of minimum rate guarantees. We study the class of greedy algorithms that rely on criteria such as induced or received interference and signal-to-interference ratio (SIR), and a class of SIR balancing algorithms. Results show superior performance for SIR balancing resource allocation and expose the performance benefits of cross-layer design.

We study carrier assignment in a single-cell multiuser OFDM multi-carrier system so as to satisfy user rate requirements with minimal resources. Different users experience different quality in different carriers due to frequency selectivity of users' propagation channels and due to non-co-located user receivers that perceive different interference from neighboring cells across carriers. We study a static instance of the problem, specified by user carrier qualities and rate requirements. Adaptive modulation at the transmitter differentiates carriers for each user. In good quality carriers, the user satisfies per-frame rate requirements with few slots (or equivalently it satisfies its per-slot rate requirements with small occupied time slot portion). We study integral and fractional assignment, where a user is assigned to only one or several carriers. Fractional assignment is formulated as a linear programming problem. For integral assignment, we introduce two classes of iterative heuristics that use carrier reassignment to users and user substitution in carriers respectively and may be viewed as resulting from corresponding optimal fractional assignment algorithms. We use Lagrangian relaxation to obtain performance bounds and show that the two classes of heuristics arise from two relaxations. Our approach identifies efficient feasible solutions and is amenable to distributed implementation.

The widespread deployment of wireless networks and hot spots that employ the IEEE 802.11 technology has forced network designers to put emphasis on the importance of ensuring efficient and fair use of network resources. In this work we propose a novel framework for detection of intelligent adaptive adversaries in the IEEE 802.11 MAC by addressing the problem of detection of the worst-case scenario attacks. Utilizing the nature of this protocol we employ sequential detection methods for detecting greedy behavior and illustrate their performance for detection of least favorable attacks. By using robust statistics in our problem formulation, we attempt to utilize the precision given by parametric tests, while avoiding the specification of the adversarial distribution. This approach establishes the lowest performance bound of a given Intrusion Detection System (IDS) in terms of detection delay and is applicable in online detection systems where users who pay for their services want to obtain the information about the best and the worst case scenarios and performance bounds of the system. This framework is meaningful for studying misbehavior due to the fact that it does not focus on specific adversarial strategies and therefore is applicable to a wide class of adversarial strategies.

We consider a scenario where a sophisticated jammer jams an area in a single-channel wireless sensor network. The jammer controls the probability of jamming and transmission range to cause maximal damage to the network in terms of corrupted communication links. The jammer action ceases when it is detected by a monitoring node in the network, and a notification message is transferred out of the jamming region. The jammer is detected at a monitor node by employing an optimal detection test based on the percentage of incurred collisions. On the other hand, the network computes channel access probability in an effort to minimize the jamming detection plus notification time. In order for the jammer to optimize its benefit, it needs to know the network channel access probability and number of neighbors of the monitor node. Accordingly, the network needs to know the jamming probability of the jammer. We study the idealized case of perfect knowledge by both the jammer and the network about the strategy of one another, and the case where the jammer or the network lack this knowledge. The latter is captured by formulating and solving optimization problems, the solutions of which constitute best responses of the attacker or the network to the worst-case strategy of each other. We also take into account potential energy constraints of the jammer and the network. We extend the problem to the case of multiple observers and adaptable jamming transmission range and propose a intuitive heuristic jamming strategy for that case.

Next generation wireless CDMA systems will provide high and variable data rates by using multicode structures, controllable code spreading gains, and transmit power adaptation. We address the emerging resource allocation problem in that context and consider maximizing the total achievable user rate while satisfying minimum rate requirements of users. We devise symbol-synchronous and asynchronous models that capture different communication scenarios. The synchronous model holds for down-link transmission in one cell. The asynchronous model captures up-link single-cell communication and down-link or up-link scenarios in multicell systems. It can also account for multipath with each path corresponding to a virtual user. We propose a class of two-stage resource allocation algorithms. First, an admissible set of codes is constructed with criteria that capture code cross-correlation, induced interference to the system, and code rates. Next, the codes are allocated to users so as to satisfy their rate requirements. In the synchronous case, the problem structure allows the distinction of the two stages. In the asynchronous case, this distinction is not feasible due to different user delay profiles perceived at the receiver. Our models and numerical results indicate interesting trends and lead to useful conclusions and design guidelines for resource allocation algorithms under the aforementioned regimes.

In recent years, it has become common consensus that independent consideration of communication layers often turns out to be inadequate in terms of providing the desired quality of service (QoS) and power efficiency in wireless networks. The need for a synergistic, cross-layer design framework has already been identified. In that respect, our work constitutes an important step towards a better understanding of the cross-layer paradigm by simultaneously targeting both the power efficiency and the end-to-end QoS in multi-hop wireless networks. More specifically, we address the joint problem of power control and scheduling with the objective of minimizing the total transmit power subject to the end-to-end bandwidth guarantees and the bit error rate constraints of each communication session. After identifying the inherent difficulty of the problem, we propose two classes of heuristic algorithms that rely on graph theory principles as well as on derived metrics such as effective interference. The first heuristic follows a top-down design strategy by solving the schedule feasibility problem as the initial step and then targeting the total power efficiency. On the other hand, the second heuristic follows a bottom-up approach that schedules one wireless link at a time by greedily filling up the available time slots. The simulation results reveal valuable insights about the performance of each strategy. The top-down design strategy turns out to address power efficiency issues better, whereas the bottom-up design strategy with a properly selected cost function for link scheduling shows better performance in finding a feasible solution, namely one that satisfies both the QoS and the transmit power constraints. Our results also illustrate the impact of routing decisions on the feasibility and the power efficiency of multi-hop wireless networks through employing different routing criteria in the experiments

Efficient use of energy while providing an adequate level of connection to individual sessions is of paramount importance in multi-hop wireless networks. Energy efficiency and connection quality depend on mechanisms that span several communication layers due to the existing co-channel interference among competing flows that must reuse the limited radio spectrum. Although independent consideration of these layers simplifies the system design, it is often insufficient for wireless networks when the overall system performance is examined carefully. The multi-hop wireless extensions and the need for routing users' sessions from source to the destination only intensify this point of view. In this work, we present a framework for cross-layer design towards energy-efficient communication. Our approach is characterized by a synergy between the physical and the medium access control (MAC) layers with a view towards inclusion of higher layers as well. More specifically, we address the joint problem of power control and scheduling with the objective of minimizing the total transmit power subject to the end-to-end quality of service (QoS) guarantees for sessions in terms of their bandwidth and bit error rate guarantees. Bearing to the NP-hardness of this combinatorial optimization problem, we propose our heuristic solutions that follow greedy approaches.

Recent advances in the area of wireless communications have revealed the emerging need for efficient wireless access in personal, local and wide area networks. Space division multiple access (SDMA) with smart antennas at the base station is recognized as a promising means of increasing system capacity and supporting rate-demanding services. However, the existence of SDMA at the physical layer raises significant issues at higher layers. In this paper, we attempt to capture the impact of SDMA on channel allocation at the media access control (MAC) layer. This impact obtains different forms in TDMA, CDMA and OFDMA access schemes, due to the different cochannel and interchannel interference instances, as well as the different effect of corresponding channels (time slots, codes or subcarrier frequencies) on user channel characteristics. We follow a unified approach for these multiple access schemes and propose heuristic algorithms to allocate channels to users and adjust down-link beamforming vectors and transmission powers, with the objective to increase achievable system rate and provide QoS to users in the form of minimum rate guarantees. We consider the class of greedy algorithms, based on criteria such as minimum induced or received interference and minimum signal-to-interference ratio (SIR), as well as the class of SIR balancing algorithms. Our results indicate that this cross-layer approach yields significant performance benefits and that SIR balancing algorithms achieves the best performance.

The use of smart antennas in extending coverage range and capacity of wireless networks dictates the employment of novel media access control protocols, with which the base station (BS) or access point (AP) provides access to users by learning their locations. We consider the class of protocols that employ beam forming and use contention-based or contention-free polling methods to locate users residing in or out of coverage range of the AP. Such protocols allow rapid media access and can be embedded in existing MAC protocols.

QoS provisioning to users in the presence of volatility of the wireless channel is the most challenging issue in wireless system design. We consider the problem of scheduling constant bit rate (CBR) traffic packets over the wireless channel, subject to packet delivery deadline constraints. We cast the problem as a Markov decision process and derive the optimal scheduling policy, in the sense of minimizing long-term packet loss due to deadline expirations. Performance bounds and design guidelines for general scheduling algorithms are obtained through analysis and simulations.

The increasing popularity of wireless broadband access in local and wide area networks is the main expression of the need for flexible and ubiquitous wireless connectivity. In order to satisfy user resource requirements in the presence of volatility of the wireless medium, sophisticated multiple access and adaptation techniques are required, which alleviate channel impairments and increase system throughput. The use of multiple antennas at the base station allows intra-cell channel reuse by multiple spatially separable users through space division multiple access (SDMA) and hence enhances cell capacity. However, the employment of antennas in the physical layer raises significant issues in the medium access control (MAC) layer. We investigate the impact of antenna arrays on MAC layer channel allocation in the context of orthogonal frequency division multiplexing (OFDM), which is the predominantly proposed signaling scheme for wireless broadband access. We propose an algorithm to allocate channels to users based on their spatial separability properties, while appropriately adjusting beamforming weights and transmission rates for each user in a channel. The unified consideration of such adaptive techniques yields significant throughput benefits.

In the near future, existing terrestrial radio networks are envisioned to integrate with satellite systems in order to provide global coverage. In order to establish communication for both nonhand-held and hand-held user terminals, the radio link design must allow full- and half-duplex operation, respectively, where the latter is desirable when radiation power restrictions are imposed. In addition, due to user mobility and wireless channel volatility, sophisticated resource management is required, so as to enhance system capacity. However, a major inherent problem of the satellite link is propagation delay, which may lead to inefficient resource allocation and reduced spectral efficiency. We address the resource allocation problem that arises in the context of a medium-Earth-orbit (MEO) satellite system with half-duplex communication capabilities. MEO satellite systems are characterized by large propagation delays and large intrabeam delay variations, which are shown to result in resource consumption. We propose a channel classification scheme, in which the available carriers are partitioned into classes and each class is associated with a range of propagation delays to the satellite. The suggested infrastructure results in better channel utilization and reduced call blocking rate and can be implemented with low signaling load.

Wireless broadband access is becoming increasingly popular in the telecommunications market due to the projected demand for high data-rate connections. Given the inherent volatility of the wireless channel, the accurate estimation of channel conditions and the adoption of sophisticated adaptation techniques is required, so that transmission parameters are selected based on link quality, and user throughput is maximized. We consider a simple wireless link monitoring method, which is based on counting positive and negative acknowledgments (ACKs and NACKs), and we investigate the class of adaptation policies that correspond to this method. The policy that maximizes the long-term throughput efficiency of a user is shown to be of threshold type. Due to inherent difficulties in realization of this policy, a suboptimal heuristic method to perform link adaptation is provided. Our results indicate a considerable improvement in link throughput under such adaptive techniques.

Wireless broadband access is an appealing solution to the projected trend towards reliable and easily deployable high-speed connections. In order to enhance system capacity and tolerate volatility of the wireless medium, sophisticated adaptation techniques are required. In this paper, we consider the problem of efficient resource allocation with adaptive modulation techniques in a multi-carrier wireless cellular system. We identify the inherent complexity of the problem and propose a heuristic algorithm for carrier frequency assignment to users, based on channel quality. The algorithm leads to an efficient allocation, in the sense that each user is assigned to a carrier and occupies the least number of channels (timeslots). Simulation results show that the algorithm leads to high link utilization and low blocking rate for a wide range of traffic loads and interference levels.

Wireless broadband access is becoming increasingly popular in the telecommunications market due to the projected demand for flexible and easily deployable high, speed connections. In order to adhere to the volatility of the wireless medium, the adoption of sophisticated adaptation techniques is required. We investigate the problem of enhancing channel throughput by performing resource assignment and reuse with adaptation of physical layer parameters. We propose an algorithm to allocate channels to users with different rate requirements, while appropriately adjusting the modulation level and transmission power, based on instantaneous channel quality. Our algorithm constructs the cochannel set of users in a sequential manner, by utilizing a criterion which is based on the induced and received amounts of interference for a user and the contribution in throughput increase. Although illustrated in the context of TDMA/TDD, the proposed technique can be applied in systems which support different multiple access and signaling schemes with orthogonal channels (e.g. OFDMA, CDMA). Our results indicate a considerable increase in throughput per utilized channel under such adaptive techniques.

In a wireless ad-hoc network environment, it is important for a mobile host to be able to broadcast control information within a team of nodes in a bandwidth-efficient and timely manner. Although the classical flooding strategy is the only reliable broadcast method for high rates of topology change, it leads to unnecessary bandwidth consumption for low rates of topology change. In this paper we propose two efficient, low complexity, loop-free broadcast routing protocols which outperform flooding in terms of bandwidth consumption in cases of low mobility and are quite robust under moderate mobility scenarios. Comparative results are obtained in environments with unpredictable link failures or lack of global topology knowledge due to node mobility.

Overlapping cell coverage areas come into play in all mobile cellular systems, especially in small-cell, high-capacity microcellular networks. Calls arising in the overlap area have access to channels of more than one base station and can select the appropriate base station to establish a connection. In this case the problems of base station and channel assignment arise jointly. We address the joint problem in a linear cellular network and prove that its solution reduces to that of a plain channel allocation problem in an equivalent linear network, after executing a sequential load balancing algorithm. The algorithm achieves optimal performance in terms of number of utilized channels and can serve as a benchmark policy for more general channel allocation procedures.