Energy-efficient MAC protocol for wireless sensor networks

A Wireless Sensor Network (WSN) is a collection of tiny devices called sensor nodes which are deployed in an area to be monitored. Each node has one or more sensors with which they can measure the characteristics of their surroundings. In a typical WSN, the data gathered by each node is sent wirelessly through the network from one node to the next towards a central base station. Each node typically has a very limited energy supply. Therefore, in order for WSNs to have acceptable lifetimes, energy efficiency is a design goal that is of utmost importance and must be kept in mind at all levels of a WSN system. The main consumer of energy on a node is the wireless transceiver and therefore, the communications that occur between nodes should be carefully controlled so as not to waste energy. The Medium Access Control (MAC) protocol is directly in charge of managing the transceiver of a node. It determines when the transceiver is on/off and synchronizes the data exchanges among neighbouring nodes so as to prevent collisions etc., enabling useful communications to occur. The MAC protocol thus has a big impact on the overall energy efficiency of a node. Many WSN MAC protocols have been proposed in the literature but it was found that most were not optimized for the group of WSNs displaying very low volumes of traffic in the network. In low traffic WSNs, a major problem faced in the communications process is clock drift, which causes nodes to become unsynchronized. The MAC protocol must overcome this and other problems while expending as little energy as possible. Many useful WSN applications show low traffic characteristics and thus a new MAC protocol was developed which is aimed at this category of WSNs. The new protocol, Dynamic Preamble Sampling MAC (DPS-MAC) builds on the family of preamble sampling protocols which were found to be most suitable for low traffic WSNs. In contrast to the most energy efficient existing preamble sampling protocols, DPS-MAC does not cater for the worst case clock drift that can occur between two nodes. Rather, it dynamically learns the actual clock drift experienced between any two nodes and then adjusts its operation accordingly. By simulation it was shown that DPS-MAC requires less protocol overhead during the communication process and thus performs more energy efficiently than its predecessors under various network operating conditions. Furthermore, DPS-MAC is less prone to become overloaded or unstable in conditions of high traffic load and high contention levels respectively. These improvements cause the use of DPS-MAC to lead to longer node and network lifetimes, thus making low traffic WSNs more feasible.Dissertation (MEng)--University of Pretoria, 2008.Electrical, Electronic and Computer EngineeringMEngUnrestricte

Road-based routing in vehicular ad hoc networks

Vehicular ad hoc networks (VANETs) can provide scalable and cost-effective solutions for applications such as traffic safety, dynamic route planning, and context-aware advertisement using short-range wireless communication. To function properly, these applications require efficient routing protocols. However, existing mobile ad hoc network routing and forwarding approaches have limited performance in VANETs. This dissertation shows that routing protocols which account for VANET-specific characteristics in their designs, such as high density and constrained mobility, can provide good performance for a large spectrum of applications. This work proposes a novel class of routing protocols as well as three forwarding optimizations for VANETs. The Road-Based using Vehicular Traffic (RBVT) routing is a novel class of routing protocols for VANETs. RBVT protocols leverage real-time vehicular traffic information to create stable road-based paths consisting of successions of road intersections that have, with high probability, network connectivity among them. Evaluations of RBVT protocols working in conjunction with geographical forwarding show delivery rate increases as much as 40% and delay decreases as much as 85% when compared with existing protocols. Three optimizations are proposed to increase forwarding performance. First, one- hop geographical forwarding is improved using a distributed receiver-based election of next hops, which leads to as much as 3 times higher delivery rates in highly congested networks. Second, theoretical analysis and simulation results demonstrate that the delay in highly congested networks can be reduced by half by switching from traditional FIFO with Taildrop queuing to LIFO with Frontdrop queuing. Third, nodes can determine suitable times to transmit data across RBVT paths or proactively replace routes before they break using analytical models that accurately predict the expected road-based path durations in VANETs

The Internet of Everything

In the era before IoT, the world wide web, internet, web 2.0 and social media made people’s lives comfortable by providing web services and enabling access personal data irrespective of their location. Further, to save time and improve efficiency, there is a need for machine to machine communication, automation, smart computing and ubiquitous access to personal devices. This need gave birth to the phenomenon of Internet of Things (IoT) and further to the concept of Internet of Everything (IoE)

The Internet of Everything

In the era before IoT, the world wide web, internet, web 2.0 and social media made people’s lives comfortable by providing web services and enabling access personal data irrespective of their location. Further, to save time and improve efficiency, there is a need for machine to machine communication, automation, smart computing and ubiquitous access to personal devices. This need gave birth to the phenomenon of Internet of Things (IoT) and further to the concept of Internet of Everything (IoE)

Design and Evaluation of Online Fault Diagnosis Protocols forwireless Networks

Any node in a network, or a component of it may fail and show undesirable behavior due to physical defects, imperfections, or hardware and/or software related glitches. Presence of faulty hosts in the network affects the computational efficiency, and quality of service (QoS). This calls for the development of efficient fault diagnosis protocols to detect and handle faulty hosts. Fault diagnosis protocols designed for wired networks cannot directly be propagated to wireless networks, due to difference in characteristics, and requirements. This thesis work unravels system level fault diagnosis protocols for wireless networks, particularly for Mobile ad hoc Networks (MANETs), and Wireless Sensor Networks (WSNs), considering faults based on their persistence (permanent, intermittent, and transient), and node mobility. Based on the comparisons of outcomes of the same tasks (comparison model ), a distributed diagnosis protocol has been proposed for static topology MANETs, where a node requires to respond to only one test request from its neighbors, that reduces the communication complexity of the diagnosis process. A novel approach to handle more intractable intermittent faults in dynamic topology MANETs is also discussed.Based on the spatial correlation of sensor measurements, a distributed fault diagnosis protocol is developed to classify the nodes to be fault-free, permanently faulty, or intermittently faulty, in WSNs. The nodes affected by transient faults are often considered fault-free, and should not be isolated from the network. Keeping this objective in mind, we have developed a diagnosis algorithm for WSNs to discriminate transient faults from intermittent and permanent faults. After each node finds the status of all 1-hop neighbors (local diagnostic view), these views are disseminated among the fault-free nodes to deduce the fault status of all nodes in the network (global diagnostic view). A spanning tree based dissemination strategy is adopted, instead of conventional flooding, to have less communication complexity. Analytically, the proposed protocols are shown to be correct, and complete. The protocols are implemented using INET-20111118 (for MANETs) and Castalia-3.2 (forWSNs) on OMNeT++ 4.2 platform. The obtained simulation results for accuracy and false alarm rate vouch the feasibility and efficiency of the proposed algorithms over existing landmark protocols

Machine Learning Meets Communication Networks: Current Trends and Future Challenges

The growing network density and unprecedented increase in network traffic, caused by the massively expanding number of connected devices and online services, require intelligent network operations. Machine Learning (ML) has been applied in this regard in different types of networks and networking technologies to meet the requirements of future communicating devices and services. In this article, we provide a detailed account of current research on the application of ML in communication networks and shed light on future research challenges. Research on the application of ML in communication networks is described in: i) the three layers, i.e., physical, access, and network layers; and ii) novel computing and networking concepts such as Multi-access Edge Computing (MEC), Software Defined Networking (SDN), Network Functions Virtualization (NFV), and a brief overview of ML-based network security. Important future research challenges are identified and presented to help stir further research in key areas in this direction

Security techniques for sensor systems and the Internet of Things

Sensor systems are becoming pervasive in many domains, and are recently being generalized by the Internet of Things (IoT). This wide deployment, however, presents significant security issues. We develop security techniques for sensor systems and IoT, addressing all security management phases. Prior to deployment, the nodes need to be hardened. We develop nesCheck, a novel approach that combines static analysis and dynamic checking to efficiently enforce memory safety on TinyOS applications. As security guarantees come at a cost, determining which resources to protect becomes important. Our solution, OptAll, leverages game-theoretic techniques to determine the optimal allocation of security resources in IoT networks, taking into account fixed and variable costs, criticality of different portions of the network, and risk metrics related to a specified security goal. Monitoring IoT devices and sensors during operation is necessary to detect incidents. We design Kalis, a knowledge-driven intrusion detection technique for IoT that does not target a single protocol or application, and adapts the detection strategy to the network features. As the scale of IoT makes the devices good targets for botnets, we design Heimdall, a whitelist-based anomaly detection technique for detecting and protecting against IoT-based denial of service attacks. Once our monitoring tools detect an attack, determining its actual cause is crucial to an effective reaction. We design a fine-grained analysis tool for sensor networks that leverages resident packet parameters to determine whether a packet loss attack is node- or link-related and, in the second case, locate the attack source. Moreover, we design a statistical model for determining optimal system thresholds by exploiting packet parameters variances. With our techniques\u27 diagnosis information, we develop Kinesis, a security incident response system for sensor networks designed to recover from attacks without significant interruption, dynamically selecting response actions while being lightweight in communication and energy overhead

Internet of Underwater Things and Big Marine Data Analytics -- A Comprehensive Survey

The Internet of Underwater Things (IoUT) is an emerging communication ecosystem developed for connecting underwater objects in maritime and underwater environments. The IoUT technology is intricately linked with intelligent boats and ships, smart shores and oceans, automatic marine transportations, positioning and navigation, underwater exploration, disaster prediction and prevention, as well as with intelligent monitoring and security. The IoUT has an influence at various scales ranging from a small scientific observatory, to a midsized harbor, and to covering global oceanic trade. The network architecture of IoUT is intrinsically heterogeneous and should be sufficiently resilient to operate in harsh environments. This creates major challenges in terms of underwater communications, whilst relying on limited energy resources. Additionally, the volume, velocity, and variety of data produced by sensors, hydrophones, and cameras in IoUT is enormous, giving rise to the concept of Big Marine Data (BMD), which has its own processing challenges. Hence, conventional data processing techniques will falter, and bespoke Machine Learning (ML) solutions have to be employed for automatically learning the specific BMD behavior and features facilitating knowledge extraction and decision support. The motivation of this paper is to comprehensively survey the IoUT, BMD, and their synthesis. It also aims for exploring the nexus of BMD with ML. We set out from underwater data collection and then discuss the family of IoUT data communication techniques with an emphasis on the state-of-the-art research challenges. We then review the suite of ML solutions suitable for BMD handling and analytics. We treat the subject deductively from an educational perspective, critically appraising the material surveyed.Comment: 54 pages, 11 figures, 19 tables, IEEE Communications Surveys & Tutorials, peer-reviewed academic journa

Intelligent Sensor Networks

In the last decade, wireless or wired sensor networks have attracted much attention. However, most designs target general sensor network issues including protocol stack (routing, MAC, etc.) and security issues. This book focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on their world-class research, the authors present the fundamentals of intelligent sensor networks. They cover sensing and sampling, distributed signal processing, and intelligent signal learning. In addition, they present cutting-edge research results from leading experts