Sensing has become a fundamental component of modern network infrastructures, powering applications from environmental monitoring to industrial automation, and bridging the gap between digital systems and the physical world. Given the large amount of data generated by these systems, it is important to find strategies that are able to intelligently manage the flow of data between all interconnected devices, in order to reduce the utilized bandwidth to a minimum. In this paper, we study the use of Wireless Edge Caching (WEC) techniques to reduce latency and optimize bandwidth and energy consumption of sensing applications. We study a specific sensing scenario where a network of wireless sensors spread over a vast territory continuously collects data, part of which must be transmitted to a central base station. To aid and enhance the performance of this application, we employ the use of WEC techniques. Since the frequency of data queries is related to the sensors’ location, we introduce a novel caching algorithm, Closest In Farthest Out (CIFO), tailored to this scenario. CIFO is able to capture the characteristics of the sensing application and perform cache eviction decisions accordingly. We demonstrate the performance of our solution by implementing it in a simulated environment and comparing it to traditional caching strategies, showing how our solution is able to outperform the other strategies under multiple settings and different metrics.

Large-scale IT infrastructures are highly energy-intensive systems. To mitigate the environmental impact of networks, it is crucial to design energy-aware traffic engineering strategies that make the best use of a network’s redundancy in order to maximize energy savings. The Software-Defined Networking (SDN) paradigm is a powerful tool that eases network management by decoupling the control and data planes. Because of its flexibility and controllability, SDN is nowadays widely adopted in data centers and enterprise networks. In this paper, we provide energy-aware traffic solutions for SDNs. We conducted extensive experiments on real switches for a thorough power consumption characterisation. Thanks to this preliminary study, we could fully characterize the solution space of a performance-constrained energy optimization problem. We formulate two optimization problems for the selective activation of switches and ports under hard constraints on traffic demand, considering both a static and dynamic traffic scenario. We show that the proposed problems are NP-hard and provide two polynomial-time heuristics for the static and dynamic case, respectively. Through simulations, we show that our solution outperforms previous approaches in all the considered settings.

Flying ad-hoc networks (FANETs) are a powerful tool for inspecting safety-critical scenarios, including post-disaster areas or military fields, where they ensure prompt area monitoring and fast detection of events of interest. However, wide area deployment of FANETs requires fast and reliable communications among devices and their base station to ensure prompt intervention upon detection of anomalies. Existing long-range communication technologies are inadequate to meet the data rate requirements and delay constraints of safety-critical applications. Previous solutions to enable ad-hoc communications in mobile networks also fall short of exploiting the controllable mobility of FANETs. To face this challenge, we formulate the connected deployment problem, where we require the FANET to dynamically create connected coverage formations to ensure multi-hop low-latency communications while performing the monitoring task. We show that addressing the above problem under the joint requirement of maximizing event coverage is NP-hard. We propose a joint Task Management and Routing Algorithm called TaMaRA, a polynomial-time solution based on a two-phase approximation of the problem. By means of extensive simulations and real field experiments we show that our approach outperforms existing solutions in terms of monitoring accuracy and system responsiveness.

Massive failures in communication networks result from natural disasters, heavy blackouts, and military and cyber attacks. After these events, an adequate network recovery plan is key to ensuring emergency-critical service restoration and preventing intolerable downtime and performance degradation. We tackle the problem of minimizing the time and number of interventions to sufficiently restore the communication network to support emergency services after large-scale failures. We propose Proton (Progressive RecOvery and Tomography-based mONitoring), an efficient algorithm for progressive recovery of emergency services. Unlike previous work, assuming centralized routing and complete network observability, Proton addresses the more realistic scenario in which the network relies on the existing routing protocols, and knowledge of the network state is partial and uncertain. Proton relies on Network Tomography for monitoring and acquiring information about the state of nodes and links. Simulation results on real topologies show that our algorithm outperforms previous solutions in terms of cumulative routed flow, repair costs and recovery time in static and dynamic failure scenarios.

As a novel network paradigm, Software Defined Networking (SDN) has greatly simplified network management, but also introduced new vulnerabilities. One vulnerability of particular interest is the flow table, a data structure in every SDN-enabled switch that caches flow rules from the controller to bridge the speed gap between the data plane and the control plane. Prior works have shown that an adversary-controlled host can accurately infer parameters of the flow table at its directly-connected edge switch, which can then be used to launch intelligent attacks. However, those solutions do not work for flow tables at internal switches. In this work, we develop an algorithm that can infer the different flow table sizes at internal switches by measuring the Round Trip Times (RTTs) of a path traversing these switches from one of its endpoints. A major challenge in this problem is the lack of an inferable relationship between the RTTs and the flow table hits/misses at the traversed switches. Our solution addresses this challenge by experimentally identifying the inferable information and designing an inference algorithm that combines carefully designed probing sequences and statistical tools to mitigate measurement noise and interference. The efficacy of our solution is validated through experiments in Mininet.

Massive failures in communication networks are a consequence of natural disasters, heavy blackouts, military and cyber attacks. We tackle the problem of minimizing the time and number of interventions to sufficiently restore the communication network so as to support emergency services after large-scale failures. We propose PRoTOn (Progressive RecOvery and Tomography-based mONitoring), an efficient algorithm for progressive recovery of emergency services. Unlike previous work, assuming centralized routing and complete network observability, PRoTOn addresses the more realistic scenario in which the network relies on the existing routing protocols, and knowledge of the network state is partial and uncertain. Simulation results carried out on real topologies show that our algorithm outperforms previous solutions in terms of cumulative routed flow, repair costs and recovery time in both static and dynamic failure scenarios.

Swarms of Unmanned Aerial Vehicles (UAVs) are a key technology to support communication in many harsh environments where fixed infrastructures (e.g., 5G) are disrupted or not available. However, the fast mobility and highly dynamic network topology pose unique challenges and require the development of novel multi-hop routing protocols. Previous work in this direction extends geographical protocols or adapts approaches designed for Mobile Ad-hoc NETworks (MANETs), rarely taking full advantage of UAV capabilities. In this paper, we introduce a novel data offloading approach, namely Stop & Offload, that exploits the device controllable mobility to facilitate network routing. The swarm of UAVs performs data offloading synchronously and recurrently. At fixed intervals of time, the swarm interrupts the sensing mission (Stop) and moves, as little as possible, to build a connected formation to the base station and offload the data (Offload). We provide both centralized solutions — assuming a long-range control channel — and a distributed solution — working in the absence of a control channel. By means of extensive simulations we show that our proposals outperform state-of-the-art solutions, decreasing the time taken to build a connected formation of about 45% and increasing the time spent on sensing of 10%. Additionally, we compared our protocol with various routing strategies and observe remarkable improvements, including a 50% reduction in average packet delay.

Aerial drones are increasingly used to perform monitoring tasks in a large number of applications. Current solutions to trajectory planning rely on perfect knowledge of ongoing events requiring inspection. Nevertheless, in many scenarios the events’ time and position can only be estimated with someuncertainty. Unlike previous work, we consider critical scenarios where a squad of drones is required to autonomously inspect an area of interest underuncertaintyof time and location of target events. The main goal of the squad is to ensure maximum coverage of event monitoring with minimum average inspection delay. With no initial knowledge, the drones share their local observations of the environment and apply the Parzen-Rosenblatt approach to manage a dynamic probabilistic map of ongoing events. This map is integrated into a virtual force approach for a joint solution to distributed dynamic trajectory planning and collision avoidance. Through extensive simulations and real-field experiments, we compare our proposal againstAC-GAP, a state-of-art solution for UAVs, andSweep, a sweep-based algorithm for multiple robots. We show that our proposal discovers new events 30-40$\%$faster than the other algorithms, and outperforms them in terms of percentage of visited events and inspection delay, under a wide variety of scenarios.

Boolean Network Tomography (BNT) aims at identifying failures of internal network components by means of end-to-end monitoring paths. However, when the number of failures is not known a priori, failure identification may require a huge number of monitoring paths. We address this problem by designing a Bayesian approach that progressively selects the next path to probe on the basis of its expected information utility, conditioned on prior observations. As the complexity of the computation of posterior probabilities of node failures is exponential in the number of failed paths, we propose a polynomial-time greedy strategy which approximates these values. To consider aging of information in dynamic failure scenarios where node states can change during a monitoring period, we propose a monitoring technique based on a sliding observation window of adaptive length. By means of numerical experiments conducted on real network topologies we demonstrate the practical applicability of our approach, and the superiority of our algorithms with respect to state of the art solutions based on classic BNT as well as sequential group testing.

In most of the developing countries, the economy is largely based on agriculture. The poor availability of skilled personnel and of appropriate supporting infrastructure, make crop fields vulnerable to the outbreak of plant diseases, possibly due to spreading viruses and fungi, or to adverse environmental conditions, such as drought. The mobile application PlantVillage Nuru provides an invaluable tool for early detection of plant diseases and sustainable food production. A mobile device endowed with Nuru is a powerful mobile sensor: it analyzes plant images and uses an AI engine to recognize health issues. In this article, we propose a crowdsensing framework, where Nuru is adopted at large scale in the farmer population. We tackle the device deployment problem, where device mobility is only partially controllable, mostly in an indirect manner, through incentives. We propose two problem formulations, and related algorithms, to minimize the number of required smartphones while providing sufficient geographical coverage. We study the proposed models in simulated as well as real scenarios, showing that they outperform the current solutions in terms of monitoring accuracy and completeness, with lower cost. Then, we describe the testbed implementation, confirming the applicability of the proposed crowdsensing framework in a real scenario in Kenya.

The fast and unconstrained mobility of Flying Ad-hoc NETworks (FANETs) brings about the need to develop solutions for packet routing in a highly dynamic topology scenario. Previous works in this direction aim at extending protocols designed for Mobile Ad-hoc NETworks (MANETs) to the more challenging domain of FANETs. Unlike previous approaches, we aim at exploiting the device controllable mobility to facilitate network routing. We propose MAD (Movement Assisted Delivery): a packet routing protocol specifically tailored for networks of aerial vehicles. MAD enables adaptive selection of the most suitable relay nodes for packet delivery, resorting to movement-assisted delivery upon need, which is supported by a reinforcement learning approach. By means of extensive simulations we show that MAD outperforms previous solutions in all the considered performance metrics including average packet delay, delivery ratio, and communication overhead, at the expense of a moderate loss in average device availability.

Boolean Network Tomography (BNT) allows to localize network failures by means of end-to-end monitoring paths. Nevertheless, it falls short of providing efficient failure identification in real scenarios, due to the large combinatorial size of the solution space, especially when multiple failures occur concurrently. We aim at maximizing the identification capabilities of a bounded number of monitoring probes. To tackle this problem we propose a progressive approach to failure localization based on stochastic optimization, whose solution is the optimal sequence of monitoring paths to probe. We address the complexity of the problem by proposing a greedy strategy in two variants: one considers exact calculation of posterior probabilities of node failures given the observation, whereas the other approximates these values through a novel failure centrality metric. We discuss the approximation of the proposed approaches. Then, by means of numerical experiments conducted on real network topologies, we demonstrate the practical applicability of our approach. The performance evaluation evidences the superiority of our algorithms with respect to state of the art solutions based on classic Boolean Network Tomography as well as approaches based on sequential group testing.

Many safety critical scenarios, including post-disaster areas, or military fields, require prompt area monitoring and fast detection of events of interest. Flying Ad-hoc Networks (FANETs) provide a powerful tool to search the area, and locate anomalies. Nevertheless, wide-area deployment of FANETs poses a number of challenges. Existing long range communication technologies are inadequate to meet the data rate and delay requirements of a safety critical application. To face this challenge, we formulate the connected deployment problem, where we require the FANET to create connected formations to ensure multi-hop low-latency communications while performing the monitoring task. We show that addressing the above problem with the aim of maximizing event coverage is NP-hard. We propose a polynomial time solution, called Greedy Connected Deployment (GCD), based on a two phase approximation of the problem. By means of extensive simulations and real field experiments, we show that our approach outperforms existing solutions to related problems, both in terms of monitoring accuracy and system responsiveness.

Fleets of cooperative drones are a powerful tool in monitoring critical scenarios requiring early anomaly discovery and intervention. Due to limited energy availability and application requirements, drones may visit target points in consecutive trips, with recharging and data offloading in between. To capture timeliness of intervention and prioritize early coverage, we propose the new notion of Weighted Progressive Coverage, which is based on the definition of time dependent weights. Weighted progressive coverage generalizes classic notions of coverage, as well as a new notion of accumulative coverage specifically designed to address trip scheduling. We show that weighted progressive coverage maximization is NP-hard and propose an efficient polynomial algorithm, called Greedy and Prune (GaP), with guaranteed approximation. By means of simulations we show that GaP performs close to the optimal solution and outperforms a previous approach in all the considered performance metrics, including coverage, average inspection delay, energy consumption, and computation time, in a wide range of application scenarios. Through prototype experiments we also confirm the theoretical and simulation analysis, and demonstrate the applicability of our algorithm in real scenarios.

Boolean network tomography is a powerful tool to infer the state (working/failed) of individual nodes from path-level measurements obtained by edge-nodes. We consider the problem of optimizing the capability of identifying network failures through the design of monitoring schemes. Finding an optimal solution is NP-hard and a large body of work has been devoted to heuristic approaches providing lower bounds. Unlike previous works, we provide upper bounds on the maximum number of identifiable nodes, given the number of monitoring paths and different constraints on the network topology, the routing scheme, and the maximum path length. These upper bounds represent a fundamental limit on identifiability of failures via Boolean network tomography. Our analysis provides insights on how to design topologies and related monitoring schemes to achieve the maximum identifiability under various network settings. Through analysis and experiments we demonstrate the tightness of the bounds and efficacy of the design insights for engineered as well as real networks.

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

We consider the problem of assigning tasks and related trajectories to a fleet of drones, in critical scenarios requiring early anomaly discovery and intervention. Drones visit target points in consecutive trips, with recharging and data offloading in between. We propose a novel metric, called weighted coverage, which generalizes classic notions of coverage, as well as a new notion of accumulative coverage which prioritizes early inspection of target points. We formulate an ILP problem for weighted coverage maximization and show its NP-hardness. We propose an efficient polynomial algorithm with guaranteed approximation. By means of simulations we show that our algorithm performs close to the optimal solution and outperforms a previous approach in terms of several performance metrics, including coverage, average inspection delay, energy consumption, and computation time, under a wide range of application scenarios.

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

Network recovery after large-scale failures has tremendous cost implications. While numerous approaches have been proposed to restore critical services after large-scale failures, they mostly assume having full knowledge of failure location, which cannot be achieved in real failure scenarios. Making restoration decisions under uncertainty is often further complicated in a large-scale failure. This paper addresses progressive network recovery under the uncertain knowledge of damages. We formulate the problem as a mixed integer linear programming and show that it is NP-hard. We propose an iterative stochastic recovery algorithm (ISR) to recover the network in a progressive manner to satisfy the critical services. At each optimization step, we make a decision to repair a part of the network and gather more information iteratively, until critical services are completely restored. We propose three different approaches: 1) an iterative shortest path algorithm; 2) an approximate branch and bound (ISR-BB); and 3) an iterative multicommodity LP relaxation (ISR-MULT). Further, we compared our approach with the state-of-the-art centrality-based damage assessment and recovery (CeDAR) and iterative split and prune (ISP) algorithms. Our results show that ISR-BB and ISR-MULT outperform the state-of-the-art ISP and CeDAR algorithms while we can configure our choice of tradeoff between the execution time, the number of repairs (cost), and the demand loss. We show that our recovery algorithm, on average, can reduce the total number of repairs by a factor of about 3 with respect to ISP, while satisfying all critical demands.

Software defined networking (SDN) provides a framework to dynamically adjust and re-program the data plane with the use of flow rules. The realization of highly adaptive SDNs with the ability to respond to changing demands or recover after a network failure in a short period of time, hinges on efficient updates of flow rules. We model the time to deploy a set of flow rules by the update time at the bottleneck switch, and formulate the problem of selecting paths to minimize the deployment time under feasibility constraints as a mixed integer linear program (MILP). To reduce the computation time of determining flow rules, we propose efficient heuristics designed to approximate the minimum-deployment-time solution by relaxing the MILP or selecting the paths sequentially. Through extensive simulations we show that our algorithms outperform current, shortest path-based solutions by reducing the total network configuration time up to 55% while having similar packet loss, in the considered scenarios. We also demonstrate that in a networked environment with a certain fraction of failed links, our algorithms are able to reduce the average time to reestablish disrupted flows by 40%.

Boolean network tomography is a powerful tool to infer the state (working/failed) of individual nodes from path-level measurements obtained by egde-nodes. We consider the problem of optimizing the capability of identifying network failures through the design of monitoring schemes. Finding an optimal solution is NP-hard and a large body of work has been devoted to heuristic approaches providing lower bounds. Unlike previous works, we provide upper bounds on the maximum number of identifiable nodes, given the number of monitoring paths and different constraints on the network topology, the routing scheme, and the maximum path length. The proposed upper bounds represent a fundamental limit on the identifiability of failures via Boolean network tomography. This analysis provides insights on how to design topologies and related monitoring schemes to achieve the maximum identifiability under various network settings. Through analysis and experiments we demonstrate the tightness of the bounds and efficacy of the design insights for engineered as well as real networks.

After a massive scale failure, the assessment of damages to communication networks requires local interventions and remote monitoring. While previous works on network recovery require complete knowledge of damage extent, we address the problem of damage assessment and critical service restoration in a joint manner. We propose a polynomial algorithm called Centrality based Damage Assessment and Recovery (CeDAR) which performs a joint activity of failure monitoring and restoration of network components. CeDAR works under limited availability of recovery resources and optimizes service recovery over time. We modified two existing approaches to the problem of network recovery to make them also able to exploit incremental knowledge of the failure extent. Through simulations we show that CeDAR outperforms the previous approaches in terms of recovery resource utilization and accumulative flow over time of the critical services.

Vulnerability due to inter-connectivity of multiple networks has been observed in many complex networks. Previous works mainly focused on robust network design and on recovery strategies after sporadic or massive failures in the case of complete knowledge of failure location. We focus on cascading failures involving the power grid and its communication network with consequent imprecision in damage assessment. We tackle the problem of mitigating the ongoing cascading failure and providing a recovery strategy. We propose a failure mitigation strategy in two steps: 1) Once a cascading failure is detected, we limit further propagation by re-distributing the generator and load's power. 2) We formulate a recovery plan to maximize the total amount of power delivered to the demand loads during the recovery intervention. Our approach to cope with insufficient knowledge of damage locations is based on the use of a new algorithm to determine consistent failure sets (CFS). We show that, given knowledge of the system state before the disruption, the CFS algorithm can find all consistent sets of unknown failures in polynomial time provided that, each connected component of the disrupted graph has at least one line whose failure status is known to the controller.

In this article, we address the problem of autonomously deploying mobile sensors in an unknown complex environment. In such a scenario, mobile sensors may encounter obstacles or environmental sources of noise, so that movement and sensing capabilities can be significantly altered and become anisotropic. Any reduction of device capabilities cannot be known prior to their actual deployment, nor can it be predicted. We propose a new algorithm for autonomous sensor movements and positioning, called DOMINO (DeplOyment of MobIle Networks with Obstacles). Unlike traditional approaches, DOMINO explicitly addresses these issues by realizing a grid-based deployment throughout the Area of Interest (AoI) and subsequently refining it to cover the target area more precisely in the regions where devices experience reduced sensing. We demonstrate the capability of DOMINO to entirely cover the AoI in a finite time. We also give bounds on the number of sensors necessary to cover an AoI with asperities. Simulations show that DOMINO provides a fast deployment with precise movements and no oscillations, with moderate energy consumption. Furthermore, DOMINO provides better performance than previous solutions in all the operative settings.

This paper addresses the problem of efficiently restoring sufficient resources in a communications network to support the demand of mission critical services after a large-scale disruption. We give a formulation of the problem as a mixed integer linear programming and show that it is NP-hard. We propose a polynomial time heuristic, called iterative split and prune (ISP) that decomposes the original problem recursively into smaller problems, until it determines the set of network components to be restored. ISP's decisions are guided by the use of a new notion of demand-based centrality of nodes. We performed extensive simulations by varying the topologies, the demand intensity, the number of critical services, and the disruption model. Compared with several greedy approaches, ISP performs better in terms of total cost of repaired components, and does not result in any demand loss. It performs very close to the optimal when the demand is low with respect to the supply network capacities, thanks to the ability of the algorithm to maximize sharing of repaired resources.

This paper addresses the problem of efficiently restoring sufficient resources in a communications network to support the demand of mission critical services after a large scale disruption. We give a formulation of the problem as an MILP and show that it is NP-hard. We propose a polynomial time heuristic, called Iterative Split and Prune (ISP) that decomposes the original problem recursively into smaller problems, until it determines the set of network components to be restored. We performed extensive simulations by varying the topologies, the demand intensity, the number of critical services, and the disruption model. Compared to several greedy approaches ISP performs better in terms of number of repaired components, and does not result in any demand loss. It performs very close to the optimal when the demand is low with respect to the supply network capacities, thanks to the ability of the algorithm to maximize sharing of repaired resources.

We consider the problem of placing services in a telecommunication network in the presence of failures. In contrast to existing service placement algorithms that focus on optimizing the quality of service (QoS), we consider the performance of monitoring failures from end-to-end connection states between clients and servers, and investigate service placement algorithms that optimize the monitoring performance subject to QoS constraints. Based on novel performance measures capturing the coverage, the identifiability, and the distinguishability in monitoring failures, we formulate the service placement problem as a set of combinatorial optimizations with these measures as objective functions. In particular, we show that maximizing the distinguishability is equivalent to minimizing the uncertainty in failure localization. We prove that all these optimizations are NP-hard. However, we show that the objectives of coverage and distinguishability have a desirable property that allows them to be approximated to a constant factor by a greedy algorithm. We further show that while the identifiability objective does not have this property, it can be approximated by the maximumdistinguishability placement in the high-identifiability regime. Our evaluations based on real network topologies verify the effectiveness of the proposed algorithms in improving the monitoring performance compared with QoS-based service placement.

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

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

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

Event monitoring is an important application of sensor networks. Multiple parties, with different surveillance targets, can share the same network, with limited sensing resources, to monitor their events of interest simultaneously. Such a system achieves profit by allocating sensing resources to missions to collect event related information (e.g., Videos, photos, electromagnetic signals). We address the problem of dynamically assigning resources to missions so as to achieve maximum profit with uncertainty in event occurrence. We consider time-varying resource demands and profits, and multiple concurrent surveillance missions. We model each mission as a sequence of monitoring attempts, each being allocated with a certain amount of resources, on a specific set of events that occurs as a Markov process. We propose a Self-Adaptive Resource Allocation algorithm (SARA) to adaptively and efficiently allocate resources according to the results of previous observations. By means of simulations we compare SARA to previous solutions and show SARA's potential in finding higher profit in both static and dynamic scenarios.

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

Mobile sensor networks enable the monitoring of remote and hostile environments without requiring human supervision. Several approaches have been proposed in the literature to let mobile sensors self-deploy over a region of interest. In this paper we study, for the first time, the vulnerabilities of one of the most referenced approaches to mobile sensor deployment, namely the Voronoi-based approach. We show that, by compromising a small number of sensors, an attacker can influence the sensor deployment causing a significant reduction of the monitoring capability of the network. We propose a secure deployment algorithm called SecureVOR. We formally prove that SecureVOR has guaranteed termination and that it allows legitimate sensors to detect the malicious behavior of compromised nodes. We also show by extensive simulations that SecureVOR is able to fulfill the network monitoring goals even in presence of an attack, at the expense of a small performance overhead.

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

In this paper we point out the vulnerabilities of the virtual force approach to mobile sensor deployment, which is at the basis of many deployment algorithms. For the first time in the literature, we show that some attacks significantly hinder the capability of these algorithms to guarantee a satisfactory coverage. An attacker can compromise a few mobile sensors and force them to pursue a malicious purpose by influencing the movement of other legitimate sensors. We make an example of a simple and effective attack, called Opportunistic Movement, and give an analytical study of its efficacy. We also show through simulations that, in a typical scenario, this attack can reduce coverage by more than 50% by compromising a number of nodes as low as the 7%. We propose SecureVF, a virtual force deployment algorithm able to neutralize the above mentioned attack. We show that under SecureVF malicious sensors are detected and then ignored whenever their movement is not compliant with the moving strategy provided by SecureVF. We also investigate the performance of SecureVF through simulations, and compare it to one of the most acknowledged algorithms based on virtual forces. We show that SecureVF enables a remarkably improved coverage of the area of interest, at the expense of a low additional energy consumption.

In order to prolong the lifetime of a wireless sensor network (WSN) devoted to monitoring an area of interest, a useful means is to exploit network redundancy, activating only the sensors that are strictly necessary for coverage and making them work with the minimum necessary sensing radius. In this article, we introduce the first algorithm that reduces sensor coverage redundancy through joint Sensor Activation and sensing Radius Adaptation (SARA) in general application scenarios comprising two classes of devices: sensors with variable sensing radius and sensors with fixed sensing radius. This device heterogeneity is explicitly addressed by modeling the coverage problem through Voronoi-Laguerre diagrams that, differently from Voronoi diagrams, allow for correctly identifying each sensor coverage region depending on the sensor current radius and the radii of its neighboring nodes. SARA executes quickly with guaranteed termination and, given the currently available nodes, it always guarantees maximum coverage. By means of extensive simulations, we show that SARA obtains remarkable improvements with respect to previous solutions, ensuring, in networks with heterogeneous nodes, longer network lifetime and wider coverage.

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

In this paper we propose GREASE, a distributed algorithm to deploy mobile sensors in an unknown environment with obstacles and field asperities that may cause sensing anisotropies and non uniform device capabilities. These aspects are not taken into account by traditional approaches to the problem of mobile sensor self-deployment. GREASE works by realizing a grid-shaped deployment throughout the Area of Interest (AoI) and adaptively refining the grid to find new sensor positions to cover the target area more precisely in the zones where devices experience reduced movement, sensing and communication capabilities. We give bounds on the number of sensors necessary to cover an AoI with obstacles and noisy zones. Simulations show that GREASE provides a fast deployment with precise movements and no oscillations, with moderate energy consumption.

In this paper we address the problem of deploying heterogeneous mobile sensors over a target area. We show how traditional approaches designed for homogeneous networks fail when adopted in the heterogeneous operative setting.

The use of mobile sensors is of great relevance for a number of strategic applications devoted to monitoring critical areas where sensors can not be deployed manually. In these networks, each sensor adapts its position on the basis of a local evaluation of the coverage efficiency, thus permitting an autonomous deployment. Several algorithms have been proposed to deploy mobile sensors over the area of interest. The applicability of these approaches largely depends on a proper formalization of rigorous rules to coordinate sensor movements, solve local conflicts and manage possible failures of communications and devices. In this paper we introduce P&P, a communication protocol that permits a correct and efficient coordination of sensor movements in agreement with the PUSH&PULL algorithm. We deeply investigate and solve the problems that may occur when coordinating asynchronous local decisions in the presence of an unreliable transmission medium and possibly faulty devices such as in the typical working scenario of mobile sensor networks. Simulation results show the performance of our protocol under a range of operative settings, including conflict situations, irregularly shaped target areas, and node failures.

Unexpected increases in demand and most of all flash crowds are considered the bane of every Web application as they may cause intolerable delays or even service unavailability. Proper quality of service policies must guarantee rapid reactivity and responsiveness even in such critical situations. Previous solutions fail to meet common performance requirements when the system has to face sudden and unpredictable surges of traffic. Indeed they often rely on a proper setting of key parameters which requires laborious manual tuning, preventing a fast adaptation of the control policies. We contribute an original self-overload control (SOC) policy. This allows the system to self-configure a dynamic constraint on the rate of admitted sessions in order to respect service level agreements and maximize the resource utilization at the same time. Our policy does not require any prior information on the incoming traffic or manual configuration of key parameters. We ran extensive simulations under a wide range of operating conditions, showing that SOC rapidly adapts to time varying traffic and self-optimizes the resource utilization. It admits as many new sessions as possible in observance of the agreements, even under intense workload variations. We compared our algorithm to previously proposed approaches highlighting a more stable behavior and a better performance.

This paper addresses the problems of admission control and server selection in a system consisting of several geographically replicated web servers and several access points. We propose a fully distributed solution in which every access point continuously monitors the availability of all server side resources, using a mixture of active and passive measurements. Based on those measures, each access point autonomously applies its decisions to the requests it receives. Admission control is performed prioritizing requests belonging to already admitted sessions, in order to maximize the chance of successfully terminating ongoing sessions. Furthermore, session information is taken into account when performing a probabilistic request redirection and server choice, in order to improve load balancing and mitigate flash crowd effects. Extensive simulations, performed in compliance with industry standards, show that our method exhibits a stable behavior during overloads and improves service quality in terms of both reduced response time and higher successful session termination.

This paper tackles the problem of autonomic admission control for web clusters. The main contribution of this work is the proposal of a new session admission algorithm that self-configures a dynamic constraint on the rate of incoming new sessions to guarantee the respect of Service Level Agreements (SLA). Unlike other approaches, our policy does not need any prior information on the incoming traffic, nor any assumption on the probability distribution of request inter-arrival or service time. Furthermore, it does not require any manual configuration or parameter tuning. We performed extensive simulations under a range of operating conditions and compared our algorithm to other previously proposed approaches. The simulations show that our policy rapidly adapts to the given traffic profile and improves service throughput while respecting the response time constraints imposed by the SLAs. It also improves service quality by reducing the oscillations of response time and number of active clients common to other policies.

The content delivery networks (CDN) paradigm is based on the idea to move third-party content closer to the users transparently. More specifically, content is replicated on servers closer to the users, and users requests are redirected to the best replica in a transparent way, so that the user perceives better content access service. In this paper we address the problem of dynamic replica placement and user requests redirection jointly. Our approach accounts for users demand variability and server constraints, and minimizes the costs paid by a CDN provider without degrading the quality of the user perceived access service. A non-linear integer programming formulation is given for the replica placement and user request redirection problems. The actual solution is obtained by mapping the non-linear integer problem into a series of mixed integer linear problems obtained by linearizing the non-linear constraints of the original problem. Preliminary numerical results show that the proposed solution is capable of effectively limiting the percentage of unsatisfied requests without over-replicating the contents over the CDN servers.

To ensure probabilistic guarantees on quality of service in content delivery networks (CDN), an access control support is needed that takes into account a proper differentiation of requests and performs session based decisions, managing different types of services and different service phases. In this paper we introduce a CDN architecture with access control capabilities at session aware access routers. We formulate a Markov modulated Poisson decision process for access control that captures the heterogeneity of multimedia services and the variable availability of resources due to the network congestions that characterize a non-dedicated network environment. The structural properties of the optimal solutions are studied and considered as the basis for the formulation of heuristics that perform close to the optimal policy.

This paper addresses the call admission control problem for the multimedia services that characterize the third generation of wireless networks. In the proposed model each cell has to serve a variety of classes of requests that differ in their traffic parameters, bandwidth requirements and in the priorities while ensuring proper quality of service levels to all of them. A semi Markov process is used to model multi-class multimedia systems with heterogeneous traffic behavior, allowing for call transitions among classes. It is shown that the derived optimal policy establishes state-related threshold values for the admission policy of handoff and new calls in the different classes, while minimizing the blocking probabilities of all the classes and prioritizing the handoff requests. It is proven that in restrictive cases the optimal policy has the shape of a multi-threshold priority policy, while in general situations the optimal policy has a more complex shape.

Abstract This paper introduces a general decision model, in the shape of a Markov Decision Process, as an instrument to analytically compare the behavior of call admission control policies. This approach allows the study of a wide class of policies, including well‐known pure stationary as well as randomized policies, in a way that explicitly incorporates the dependency between the hand‐off rate and the system state, assuming that the hand‐off rate arriving to a cell is proportional to the occupancy level of the adjacent cells. In particular, some well‐known non‐preemptive prioritization schemes are analyzed, including the Cutoff Priority Policy (CPP), which consists of reserving a number of channels for the high priority requests stream. Using our analytical approach, we prove the optimality of CPP within the analyzed class. Copyright © 2001 John Wiley & Sons, Ltd.

This paper studies call admission policies for access control in cellular networks by means of a Markov decision process (MDP). This approach allows us to study a wide class of policies, including well known pure stationary as well as randomized policies, in a way that explicitly incorporates the dependency between the hand-off rate and the system state, assuming that the hand-off rate arriving to a cell is proportional to the occupancy level of the adjacent cells. In particular, we propose and analyze a nonpreemptive prioritization scheme, we term the cutoff priority policy. This policy consists of reserving a number of channels for the high priority requests stream. Using our analytical approach, we prove the proposed scheme to be optimal within the analyzed class.