Simone Silvestri

Push & Pull: autonomous deployment of mobile sensors for a complete coverage

Mobile sensor networks are important for several strategic applications devoted to monitoring critical areas. In such hostile scenarios, sensors cannot be deployed manually and are either sent from a safe location or dropped from an aircraft. Mobile devices permit a dynamic deployment reconfiguration that improves the coverage in terms of completeness and uniformity. In this paper we propose a distributed algorithm for the autonomous deployment of mobile sensors called Push & Pull. According to our proposal, movement decisions are made by each sensor on the basis of locally available information and do not require any prior knowledge of the operating conditions or any manual tuning of key parameters. We formally prove that, when a sufficient number of sensors are available, our approach guarantees a complete and uniform coverage. Furthermore, we demonstrate that the algorithm execution always terminates preventing movement oscillations. Numerous simulations show that our algorithm reaches a complete coverage within reasonable time with moderate energy consumption, even when the target area has irregular shapes. Performance comparisons between Push & Pull and one of the most acknowledged algorithms show how the former one can efficiently reach a more uniform and complete coverage under a wide range of working scenarios.

Snap and Spread: A Self-deployment Algorithm for Mobile Sensor Networks

The use of mobile sensors is motivated by the necessity to monitor critical areas where sensor deployment cannot be performed manually. In these working scenarios, sensors must adapt their initial position to reach a final deployment which meets some given performance objectives such as coverage extension and uniformity, total moving distance, number of message exchanges and convergence rate. We propose an original algorithm for autonomous deployment of mobile sensors called Snap & Spread . Decisions regarding the behavior of each sensor are based on locally available information and do not require any prior knowledge of the operating conditions nor any manual tuning of key parameters. We conduct extensive simulations to evaluate the performance of our algorithm. This experimental study shows that, unlike previous solutions, our algorithm reaches a final stable deployment, uniformly covering even irregular target areas. Simulations also give insights on the choice of some algorithm variants that may be used under some different operative settings.

Autonomous Deployment of Self-Organizing Mobile Sensors for a Complete Coverage

In this paper we propose an algorithm for the autonomous deployment of mobile sensors over critical target areas where sensors cannot be deployed manually. The application of our approach does not require prior knowledge of the working scenario nor any manual tuning of key parameters. Our algorithm is completely distributed and sensors make movement decisions on the basis of locally available information. We prove that our approach guarantees a complete coverage, provided that a sufficient number of sensors are available. Furthermore, we demonstrate that the algorithm execution always terminates preventing movement oscillations. We compare our proposal with one of the most acknowledged algorithms by means of extensive simulations, showing that our algorithm reaches a complete and more uniform coverage under a wide range of operating conditions.

Self-* through self-learning: Overload control for distributed web systems

Overload control is a challenging problem for web-based applications, which are often prone to unexpected surges of traffic. Existing solutions are still far from guaranteeing the necessary responsiveness under rapidly changing operative conditions. We contribute an original self-* overload control (SOC) algorithm that self-configures a dynamic constraint on the rate of incoming new sessions in order to guarantee the fulfillment of the quality requirements specified in a service level agreement (SLA). Our algorithm is based on a measurement activity that makes the system capable of self-learning and self-configuring even in the case of rapidly changing traffic scenarios, dynamic resource provisioning or server faults. Unlike other approaches, our proposal does not require any prior information about the incoming traffic, or any manual configuration of key parameters. We ran extensive simulations under a wide range of operating conditions. The experiments show how the proposed system self-protects from overload, meeting SLA requirements even under intense workload variations. Moreover, it rapidly adapts to unexpected changes in available capacity, as in the case of faults or voluntary architectural adjustments. Performance comparisons with other previously proposed approaches show that our algorithm has better performance and more stable behavior.

Autonomous deployment of heterogeneous mobile sensors

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.

Mobile Sensor Deployment in Unknown Fields

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.

On Adaptive Density Deployment to Mitigate the Sink-Hole Problem in Mobile Sensor Networks

The use of mobile sensors is of great relevance to monitor critical areas where sensors cannot be deployed manually. The presence of data collector sinks causes increased energy depletion in their proximity, due to the higher relay load under multi-hop communication schemes (sink-hole phenomenon). We propose a new approach towards the solution of this problem by means of an autonomous deployment algorithm that guarantees the adaptation of the sensor density to the sink proximity and enables their selective activation. The proposed algorithm also permits a fault tolerant and self-healing deployment, and allows the realization of an integrated solution for deployment, dynamic relocation and selective sensor activation. We formally prove the termination of our algorithm. Performance comparisons between our proposal and previous approaches show how the former can efficiently reach a deployment at the desired variable density with moderate energy consumption under a wide range of operative settings.

Managing Contingencies in Smart Grids via the Internet of Things

This paper proposes a framework for contingency management using smart loads, which are realized through the emerging paradigm of the Internet of things. The framework involves the system operator, the load serving entities (LSEs), and the end-users with smart home management systems that automatically control adjustable loads. The system operator uses an efficient linear equation solver to quickly calculate the load curtailment needed at each bus to relieve congested lines after a contingency. Given this curtailment request, an LSE calculates a power allowance for each of its end-use customers to maximize the aggregate user utility. This large-scale NP-hard problem is approximated to a convex optimization for efficient computation. A smart home management system determines the appliances allowed to be used in order to maximize the user’s utility within the power allowance given by the LSE. Since the user’s utility depends on the near-future usage of the appliances, the framework provides the Welch-based reactive appliance prediction (WRAP) algorithm to predict the user behavior and maximize utility. The proposed framework is validated using the New England 39-bus test system. The results show that power system components at risk can be quickly alleviated by adjusting a large number of small smart loads. Additionally, WRAP accurately predicts the users’ future behavior, minimizing the impact on the aggregate users’ utility.

Robust Network Tomography in the Presence of Failures

In this paper, we study the problem of selecting paths to improve the performance of network tomography applications in the presence of network element failures. We model the robustness of paths in network tomography by a metric called expected rank. We formulate an optimization problem to cover two complementary performance metrics: robustness and probing cost. The problem aims at maximizing the expected rank under a budget constraint on the probing cost. We prove that the problem is NP-Hard. Under the assumption that the failure distribution is known, we propose an algorithm called RoMe with guaranteed approximation ratio. Moreover, since evaluating the expected rank is generally hard, we provide a bound which can be evaluated efficiently. We also consider the case in which the failure distribution is not known, and propose a reinforcement learning algorithm to solve our optimization problem, using RoMe as a subroutine. We run a wide range of simulations under realistic network topologies and link failure models to evaluate our solution against a state-of-the-art path selection algorithm. Results show that our approaches provide significant improvements in the performance of network tomography applications under failures.

MobiBar: Barrier Coverage with Mobile Sensors

Critical homeland security applications such as monitoring zones contaminated by chemical or biological attacks and monitoring the spread of forest fires, require the timely creation of barrier of sensors along the border to be monitored. The strict time requirements and the hazardous nature of these contexts impede manual sensor positioning. Mobile Wireless Sensor Networks have the potential to meet the desired coverage requirements, by exploiting the device locomotion capabilities. In this paper we propose MobiBar, a distributed and asynchronous algorithm for k-barrier coverage with mobile sensors. We formally prove that MobiBar terminates in a finite time and that the final deployment provides the maximum level of barrier coverage with the available sensors. We compare MobiBar to a recent virtual force-based approach by means of simulations, which show the superiority of our solution. Furthermore, we show the self-healing capability of MobiBar to quickly recover from sudden sensor faults.