Efficient computation of min and max sensor values in multihop networks

Consider a wireless sensor network (WSN) where a broadcast from a sensor node does not reach all sensor nodes in the network; such networks are often called multihop networks. Sensor nodes take individual sensor readings, however, in many cases, it is relevant to compute aggregated quantities of these readings. In fact, the minimum and maximum of all sensor readings at an instant are often interesting because they indicate abnormal behavior, for example if the maximum temperature is very high then it may be that a fire has broken out. In this context, we propose an algorithm for computing the min or max of sensor readings in a multihop network. This algorithm has the particularly interesting property of having a time complexity that does not depend on the number of sensor nodes; only the network diameter and the range of the value domain of sensor readings matter.

Fast mobility support in low-power wireless networks: smart-HOP over RPL/6LoWPAN

With the emergence of low-power wireless hardware new ways of communication were needed. In order to standardize the communication between these low powered devices the Internet Engineering Task Force (IETF) released the 6LoWPAN stand- ard that acts as an additional layer for making the IPv6 link layer suitable for the lower-power and lossy networks. In the same way, IPv6 Routing Protocol for Low- Power and Lossy Networks (RPL) has been proposed by the IETF Routing Over Low power and Lossy networks (ROLL) Working Group as a standard routing protocol for IPv6 routing in low-power wireless sensor networks. The research performed in this thesis uses these technologies to implement a mobility process. Mobility management is a fundamental yet challenging area in low-power wireless networks. There are applications that require mobile nodes to exchange data with a xed infrastructure with quality-of-service guarantees. A prime example of these applications is the monitoring of patients in real-time. In these scenarios, broadcast- ing data to all access points (APs) within range may not be a valid option due to the energy consumption, data storage and complexity requirements. An alternative and e cient option is to allow mobile nodes to perform hand-o s. Hand-o mechanisms have been well studied in cellular and ad-hoc networks. However, low-power wireless networks pose a new set of challenges. On one hand, simpler radios and constrained resources ask for simpler hand-o schemes. On the other hand, the shorter coverage and higher variability of low-power links require a careful tuning of the hand-o parameters. In this work, we tackle the problem of integrating smart-HOP within a standard protocol, speci cally RPL. The simulation results in Cooja indicate that the pro- posed scheme minimizes the hand-o delay and the total network overhead. The standard RPL protocol is simply unable to provide a reliable mobility support sim- ilar to other COTS technologies. Instead, they support joining and leaving of nodes, with very low responsiveness in the existence of physical mobility.

Reliable link quality estimation in low-power wireless networks and its impact on tree-routing

Radio link quality estimation is essential for protocols and mechanisms such as routing, mobility management and localization, particularly for low-power wireless networks such as wireless sensor networks. Commodity Link Quality Estimators (LQEs), e.g. PRR, RNP, ETX, four-bit and RSSI, can only provide a partial characterization of links as they ignore several link properties such as channel quality and stability. In this paper, we propose F-LQE (Fuzzy Link Quality Estimator, a holistic metric that estimates link quality on the basis of four link quality properties—packet delivery, asymmetry, stability, and channel quality—that are expressed and combined using Fuzzy Logic. We demonstrate through an extensive experimental analysis that F-LQE is more reliable than existing estimators (e.g., PRR, WMEWMA, ETX, RNP, and four-bit) as it provides a finer grain link classification. It is also more stable as it has lower coefficient of variation of link estimates. Importantly, we evaluate the impact of F-LQE on the performance of tree routing, specifically the CTP (Collection Tree Protocol). For this purpose, we adapted F-LQE to build a new routing metric for CTP, which we dubbed as F-LQE/RM. Extensive experimental results obtained with state-of-the-art widely used test-beds show that F-LQE/RM improves significantly CTP routing performance over four-bit (the default LQE of CTP) and ETX (another popular LQE). F-LQE/RM improves the end-to-end packet delivery by up to 16%, reduces the number of packet retransmissions by up to 32%, reduces the Hop count by up to 4%, and improves the topology stability by up to 47%.

Quality-of-service aware routing for static and mobile IPv6-based low-power and lossy sensor networks using RPL

The Internet of Things (IoT) has emerged as a paradigm over the last few years as a result of the tight integration of the computing and the physical world. The requirement of remote sensing makes low-power wireless sensor networks one of the key enabling technologies of IoT. These networks encompass several challenges, especially in communication and networking, due to their inherent constraints of low-power features, deployment in harsh and lossy environments, and limited computing and storage resources. The IPv6 Routing Protocol for Low Power and Lossy Networks (RPL) [1] was proposed by the IETF ROLL (Routing Over Low-power Lossy links) working group and is currently adopted as an IETF standard in the RFC 6550 since March 2012. Although RPL greatly satisfied the requirements of low-power and lossy sensor networks, several issues remain open for improvement and specification, in particular with respect to Quality of Service (QoS) guarantees and support for mobility. In this paper, we focus mainly on the RPL routing protocol. We propose some enhancements to the standard specification in order to provide QoS guarantees for static as well as mobile LLNs. For this purpose, we propose OF-FL (Objective Function based on Fuzzy Logic), a new objective function that overcomes the limitations of the standardized objective functions that were designed for RPL by considering important link and node metrics, namely end-to-end delay, number of hops, ETX (Expected transmission count) and LQL (Link Quality Level). In addition, we present the design of Co-RPL, an extension to RPL based on the corona mechanism that supports mobility in order to overcome the problem of slow reactivity to frequent topology changes and thus providing a better quality of service mainly in dynamic networks application. Performance evaluation results show that both OF-FL and Co-RPL allow a great improvement when compared to the standard specification, mainly in terms of packet loss ratio and average network latency. 2015 Elsevier B.V. Al

Adaptive learning in agents behaviour: a framework for electricity markets simulation

Electricity markets are complex environments with very particular characteristics. A critical issue regarding these specific characteristics concerns the constant changes they are subject to. This is a result of the electricity markets’ restructuring, which was performed so that the competitiveness could be increased, but it also had exponential implications in the increase of the complexity and unpredictability in those markets scope. The constant growth in markets unpredictability resulted in an amplified need for market intervenient entities in foreseeing market behaviour. The need for understanding the market mechanisms and how the involved players’ interaction affects the outcomes of the markets, contributed to the growth of usage of simulation tools. Multi-agent based software is particularly well fitted to analyze dynamic and adaptive systems with complex interactions among its constituents, such as electricity markets. This dissertation presents ALBidS – Adaptive Learning strategic Bidding System, a multiagent system created to provide decision support to market negotiating players. This system is integrated with the MASCEM electricity market simulator, so that its advantage in supporting a market player can be tested using cases based on real markets’ data. ALBidS considers several different methodologies based on very distinct approaches, to provide alternative suggestions of which are the best actions for the supported player to perform. The approach chosen as the players’ actual action is selected by the employment of reinforcement learning algorithms, which for each different situation, simulation circumstances and context, decides which proposed action is the one with higher possibility of achieving the most success. Some of the considered approaches are supported by a mechanism that creates profiles of competitor players. These profiles are built accordingly to their observed past actions and reactions when faced with specific situations, such as success and failure. The system’s context awareness and simulation circumstances analysis, both in terms of results performance and execution time adaptation, are complementary mechanisms, which endow ALBidS with further adaptation and learning capabilities.

Entity localization and tracking: a sensor fusion-based mechanism in WSNs

Knowing exactly where a mobile entity is and monitoring its trajectory in real-time has recently attracted a lot of interests from both academia and industrial communities, due to the large number of applications it enables, nevertheless, it is nowadays one of the most challenging problems from scientific and technological standpoints. In this work we propose a tracking system based on the fusion of position estimations provided by different sources, that are combined together to get a final estimation that aims at providing improved accuracy with respect to those generated by each system individually. In particular, exploiting the availability of a Wireless Sensor Network as an infrastructure, a mobile entity equipped with an inertial system first gets the position estimation using both a Kalman Filter and a fully distributed positioning algorithm (the Enhanced Steepest Descent, we recently proposed), then combines the results using the Simple Convex Combination algorithm. Simulation results clearly show good performance in terms of the final accuracy achieved. Finally, the proposed technique is validated against real data taken from an inertial sensor provided by THALES ITALIA.

EMMON: a system architecture for large- scale, dense and real-time WSNs

In spite of the significant amount of scientific work in Wireless Sensor Networks (WSNs), there is a clear lack of effective, feasible and usable WSN system architectures that address both functional and non-functional requirements in an integrated fashion. This poster abstract outlines the EMMON system architecture for large-scale, dense, real-time embedded monitoring. EMMON relies on a hierarchical network architecture together with integrated middleware and command&control mechanisms. It has been designed to use standard commercially– available technologies, while maintaining as much flexibility as possible to meet specific applications’ requirements. The EMMON WSN architecture has been validated through extensive simulation and experimental evaluation, including through a 300+ node test-bed, the largest WSN test-bed in Europe to date

Poster: EMMON: a WSN system architecture and toolset for large-scale and dense real-time embedded monitoring

Wireless sensor networks (WSNs) have attracted growing interest in the last decade as an infrastructure to support a diversity of ubiquitous computing and cyber-physical systems. However, most research work has focused on protocols or on specific applications. As a result, there remains a clear lack of effective and usable WSN system architectures that address both functional and non-functional requirements in an integrated fashion. This poster outlines the EMMON system architecture for large-scale, dense, real-time embedded monitoring. It provides a hierarchical communication architecture together with integrated middleware and command and control software. It has been designed to maintain as much as flexibility as possible while meeting specific applications requirements. EMMON has been validated through extensive analytical, simulation and experimental evaluations, including through a 300+ nodes test-bed the largest single-site WSN test-bed in Europe.

EMMON: a WSN system architecture for large scale and dense real-time embedded monitoring

Wireless sensor networks (WSNs) have attracted growing interest in the last decade as an infrastructure to support a diversity of ubiquitous computing and cyber-physical systems. However, most research work has focused on protocols or on specific applications. As a result, there remains a clear lack of effective, feasible and usable system architectures that address both functional and non-functional requirements in an integrated fashion. In this paper, we outline the EMMON system architecture for large-scale, dense, real-time embedded monitoring. EMMON provides a hierarchical communication architecture together with integrated middleware and command and control software. It has been designed to use standard commercially-available technologies, while maintaining as much flexibility as possible to meet specific applications requirements. The EMMON architecture has been validated through extensive simulation and experimental evaluation, including a 300+ node test-bed, which is, to the best of our knowledge, the largest single-site WSN test-bed in Europe to date.

Inter-application redundancy elimination in sensor networks with compiler-assisted scheduling

Most current-generation Wireless Sensor Network (WSN) nodes are equipped with multiple sensors of various types, and therefore support for multi-tasking and multiple concurrent applications is becoming increasingly common. This trend has been fostering the design of WSNs allowing several concurrent users to deploy applications with dissimilar requirements. In this paper, we extend the advantages of a holistic programming scheme by designing a novel compiler-assisted scheduling approach (called REIS) able to identify and eliminate redundancies across applications. To achieve this useful high-level optimization, we model each user application as a linear sequence of executable instructions. We show how well-known string-matching algorithms such as the Longest Common Subsequence (LCS) and the Shortest Common Super-sequence (SCS) can be used to produce an optimal merged monolithic sequence of the deployed applications that takes into account embedded scheduling information. We show that our approach can help in achieving about 60% average energy savings in processor usage compared to the normal execution of concurrent applications.

Hybrid wired/wireless PROFIBUS architectures: performance study based on simulation models

The problem of providing a hybrid wired/wireless communications for factory automation systems is still an open issue, notwithstanding the fact that already there are some solutions. This paper describes the role of simulation tools on the validation and performance analysis of two wireless extensions for the PROFIBUS protocol. In one of them, the Intermediate Systems, which connect wired and wireless network segments, operate as repeaters. In the other one the Intermediate Systems operate as bridge. We also describe how the analytical analysis proposed for these kinds of networks can be used for the setting of some network parameters and for the guaranteeing real-time behaviour of the system. Additionally, we also compare the bridge-based solution simulation results with the analytical results.

Exploiting a prioritized MAC protocol to efficiently compute min and max in multihop networks

Consider a wireless sensor network (WSN) where a broadcast from a sensor node does not reach all sensor nodes in the network; such networks are often called multihop networks. Sensor nodes take sensor readings but individual sensor readings are not very important. It is important however to compute aggregated quantities of these sensor readings. The minimum and maximum of all sensor readings at an instant are often interesting because they indicate abnormal behavior, for example if the maximum temperature is very high then it may be that a fire has broken out. We propose an algorithm for computing the min or max of sensor reading in a multihop network. This algorithm has the particularly interesting property of having a time complexity that does not depend on the number of sensor nodes; only the network diameter and the range of the value domain of sensor readings matter.

Real-time communications over cluster-tree sensor networks with mobile sink behaviour

Modelling the fundamental performance limits of wireless sensor networks (WSNs) is of paramount importance to understand the behaviour of WSN under worst case conditions and to make the appropriate design choices. In that direction, this paper contributes with a methodology for modelling cluster tree WSNs with a mobile sink. We propose closed form recurrent expressions for computing the worst case end to end delays, buffering and bandwidth requirements across any source-destination path in the cluster tree assuming error free channel. We show how to apply our theoretical results to the specific case of IEEE 802.15.4/ZigBee WSNs. Finally, we demonstrate the validity and analyze the accuracy of our methodology through a comprehensive experimental study, therefore validating the theoretical results through experimentation.

Adaptive learning in agents behaviour: A framework for electricity markets simulation

Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM (Multi-Agent System for Competitive Electricity Markets) is a multi-agent electricity market simulator that models market players and simulates their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. This paper presents a methodology to provide decision support to electricity market negotiating players. This model allows integrating different strategic approaches for electricity market negotiations, and choosing the most appropriate one at each time, for each different negotiation context. This methodology is integrated in ALBidS (Adaptive Learning strategic Bidding System) – a multiagent system that provides decision support to MASCEM's negotiating agents so that they can properly achieve their goals. ALBidS uses artificial intelligence methodologies and data analysis algorithms to provide effective adaptive learning capabilities to such negotiating entities. The main contribution is provided by a methodology that combines several distinct strategies to build actions proposals, so that the best can be chosen at each time, depending on the context and simulation circumstances. The choosing process includes reinforcement learning algorithms, a mechanism for negotiating contexts analysis, a mechanism for the management of the efficiency/effectiveness balance of the system, and a mechanism for competitor players' profiles definition.

Multi-Agent Based Smart Grid Management and Simulation: Situation Awareness and Learning in a Test Bed with Simulated and Real Installations and Players

The rising usage of distributed energy resources has been creating several problems in power systems operation. Virtual Power Players arise as a solution for the management of such resources. Additionally, approaching the main network as a series of subsystems gives birth to the concepts of smart grid and micro grid. Simulation, particularly based on multi-agent technology is suitable to model all these new and evolving concepts. MASGriP (Multi-Agent Smart Grid simulation Platform) is a system that was developed to allow deep studies of the mentioned concepts. This paper focuses on a laboratorial test bed which represents a house managed by a MASGriP player. This player is able to control a real installation, responding to requests sent by the system operators and reacting to observed events depending on the context.