Unified Role Assignment Framework For Wireless Sensor Networks

Wireless sensor networks are made possible by the continuing improvements in embedded sensor, VLSI, and wireless radio technologies. Currently, one of the important challenges in sensor networks is the design of a systematic network management framework that allows localized and collaborative resource control uniformly across all application services such as sensing, monitoring, tracking, data aggregation, and routing. The research in wireless sensor networks is currently oriented toward a cross-layer network abstraction that supports appropriate fine or course grained resource controls for energy efficiency. In that regard, we have designed a unified role-based service paradigm for wireless sensor networks. We pursue this by first developing a Role-based Hierarchical Self-Organization (RBSHO) protocol that organizes a connected dominating set (CDS) of nodes called dominators. This is done by hierarchically selecting nodes that possess cumulatively high energy, connectivity, and sensing capabilities in their local neighborhood. The RBHSO protocol then assigns specific tasks such as sensing, coordination, and routing to appropriate dominators that end up playing a certain role in the network. Roles, though abstract and implicit, expose role-specific resource controls by way of role assignment and scheduling. Based on this concept, we have designed a Unified Role-Assignment Framework (URAF) to model application services as roles played by local in-network sensor nodes with sensor capabilities used as rules for role identification. The URAF abstracts domain specific role attributes by three models: the role energy model, the role execution time model, and the role service utility model. The framework then generalizes resource management for services by providing abstractions for controlling the composition of a service in terms of roles, its assignment, reassignment, and scheduling. To the best of our knowledge, a generic role-based framework that provides a simple and unified network management solution for wireless sensor networks has not been proposed previously

Exploiting the power of multiplicity: a holistic survey of network-layer multipath

The Internet is inherently a multipath network: For an underlying network with only a single path, connecting various nodes would have been debilitatingly fragile. Unfortunately, traditional Internet technologies have been designed around the restrictive assumption of a single working path between a source and a destination. The lack of native multipath support constrains network performance even as the underlying network is richly connected and has redundant multiple paths. Computer networks can exploit the power of multiplicity, through which a diverse collection of paths is resource pooled as a single resource, to unlock the inherent redundancy of the Internet. This opens up a new vista of opportunities, promising increased throughput (through concurrent usage of multiple paths) and increased reliability and fault tolerance (through the use of multiple paths in backup/redundant arrangements). There are many emerging trends in networking that signify that the Internet's future will be multipath, including the use of multipath technology in data center computing; the ready availability of multiple heterogeneous radio interfaces in wireless (such as Wi-Fi and cellular) in wireless devices; ubiquity of mobile devices that are multihomed with heterogeneous access networks; and the development and standardization of multipath transport protocols such as multipath TCP. The aim of this paper is to provide a comprehensive survey of the literature on network-layer multipath solutions. We will present a detailed investigation of two important design issues, namely, the control plane problem of how to compute and select the routes and the data plane problem of how to split the flow on the computed paths. The main contribution of this paper is a systematic articulation of the main design issues in network-layer multipath routing along with a broad-ranging survey of the vast literature on network-layer multipathing. We also highlight open issues and identify directions for future work

QoS multicast routing protocol oriented to cognitive network using competitive coevolutionary algorithm

The human intervention in the network management and maintenance should be reduced to alleviate the ever-increasing spatial and temporal complexity. By mimicking the cognitive behaviors of human being, the cognitive network improves the scalability, self-adaptation, self-organization, and self-protection in the network. To implement the cognitive network, the cognitive behaviors for the network nodes need to be carefully designed. Quality of service (QoS) multicast is an important network problem. Therefore, it is appealing to develop an effective QoS multicast routing protocol oriented to cognitive network. In this paper, we design the cognitive behaviors summarized in the cognitive science for the network nodes. Based on the cognitive behaviors, we propose a QoS multicast routing protocol oriented to cognitive network, named as CogMRT. It is a distributed protocol where each node only maintains local information. The routing search is in a hop by hop way. Inspired by the small-world phenomenon, the cognitive behaviors help to accumulate the experiential route information. Since the QoS multicast routing is a typical combinatorial optimization problem and it is proved to be NP-Complete, we have applied the competitive coevolutionary algorithm (CCA) for the multicast tree construction. The CCA adopts novel encoding method and genetic operations which leverage the characteristics of the problem. We implement and evaluate CogMRT and other two promising alternative protocols in NS2 platform. The results show that CogMRT has remarkable advantages over the counterpart traditional protocols by exploiting the cognitive favors

Internet of Things-aided Smart Grid: Technologies, Architectures, Applications, Prototypes, and Future Research Directions

Traditional power grids are being transformed into Smart Grids (SGs) to address the issues in existing power system due to uni-directional information flow, energy wastage, growing energy demand, reliability and security. SGs offer bi-directional energy flow between service providers and consumers, involving power generation, transmission, distribution and utilization systems. SGs employ various devices for the monitoring, analysis and control of the grid, deployed at power plants, distribution centers and in consumers' premises in a very large number. Hence, an SG requires connectivity, automation and the tracking of such devices. This is achieved with the help of Internet of Things (IoT). IoT helps SG systems to support various network functions throughout the generation, transmission, distribution and consumption of energy by incorporating IoT devices (such as sensors, actuators and smart meters), as well as by providing the connectivity, automation and tracking for such devices. In this paper, we provide a comprehensive survey on IoT-aided SG systems, which includes the existing architectures, applications and prototypes of IoT-aided SG systems. This survey also highlights the open issues, challenges and future research directions for IoT-aided SG systems

The improvements of power management for clustered type large scope wireless sensor networks2010

Fuente Aragón, PDL. (2010). The improvements of power management for clustered type large scope wireless sensor networks2010. http://hdl.handle.net/10251/10244.Archivo delegad

Hierarchical routing protocols for wireless sensor network: a compressive survey

Wireless Sensor Networks (WSNs) are one of the key enabling technologies for the Internet of Things (IoT). WSNs play a major role in data communications in applications such as home, health care, environmental monitoring, smart grids, and transportation. WSNs are used in IoT applications and should be secured and energy efficient in order to provide highly reliable data communications. Because of the constraints of energy, memory and computational power of the WSN nodes, clustering algorithms are considered as energy efficient approaches for resource-constrained WSNs. In this paper, we present a survey of the state-of-the-art routing techniques in WSNs. We first present the most relevant previous work in routing protocols surveys then highlight our contribution. Next, we outline the background, robustness criteria, and constraints of WSNs. This is followed by a survey of different WSN routing techniques. Routing techniques are generally classified as flat, hierarchical, and location-based routing. This survey focuses on the deep analysis of WSN hierarchical routing protocols. We further classify hierarchical protocols based on their routing techniques. We carefully choose the most relevant state-of-the-art protocols in order to compare and highlight the advantages, disadvantage and performance issues of each routing technique. Finally, we conclude this survey by presenting a comprehensive survey of the recent improvements of Low-Energy Adaptive Clustering Hierarchy (LEACH) routing protocols and a comparison of the different versions presented in the literature

Relay Placement in Sensor Networks

In this thesis, I study relay placement in energy-constrained wireless sensor net-works. The goal is to optimise balanced data gathering, where the utility function is a weighted sum of the minimum and average amounts of data collected from each sensor node. I define a number of classes of simplified relay placement problems, including a planar problem with a simple cost model for radio communication. The computational complexity of these classes is studied, and all classes are proved NP-hard; in some cases even finding approximate solutions is NP-hard. I also present algorithms for finding k-optimal solutions to the relay placement problem. These algorithms have been implemented, and their performance has been studied empiri-cally; the implementation is freely available

Towards Efficient Load Balancing Strategy for RPL Routing Protocol in IoT Networks

학위논문 (석사)-- 서울대학교 대학원 : 공과대학 컴퓨터공학부, 2018. 8. Chong-Kwon Kim.The IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) has been considered as the new standard routing protocol designed to meet the requirements of wide range of Low Power and Lossy Networks (LLNs) applications including industrial and environmental monitoring, smart grid, and wireless sensor networks. However, due to the uneven deployment of sensor nodes in large-scale networks and the heterogeneous traffic patterns, some sensor nodes have much heavier workload than others. The lack of load balancing mechanism results in these sensor nodes quickly exhausting their energy, therefore shorten the network lifetime of battery-powered wireless sensor networks. To overcome this problem, we propose a skewness and load balancing routing protocol based on the RPL protocol, named SB-RPL that exploits various routing metrics including link quality and skewness among subtrees of the network in support topology construction. In this work, we first investigate the load balancing and related issues of RPL both via numerical simulations and via actual large-scale testbed. Performance analysis results show that RPL trees suffer from severe skewness regardless of routing metrics in randomly generated networks. Through extensive computer simulations and actual experiments, we demonstrate that SB-RPL significantly improves end-to-end packet delivery performance and tree balance compared to the standard RPL.Contents ABSTRACT…………………………………………………………..i Contents…………………………………………………………….iii List of Figures...……………………………………………………vi List of Tables…...…………………………………………………vii Glossary…………..…………………………………………………viii Chapter I: Introduction ................................................. 1 1.1. Overview ............................................................... 1 1.2. Motivation ............................................................. 2 1.3. Key Idea ................................................................. 4 1.4. Contribution ........................................................... 4 1.5. Thesis Organization ................................................. 6 Chapter II: Background and Literature Review ................. 7 2.1. RPL Overview .......................................................... 7 2.2. DODAG Construction ............................................... 7 2.3. Trickle Timer .............................................................10 2.4. RPL Operation Modes ...............................................11 2.5. Literature Review ......................................................11 2.5.1. RPL Objective Functions: ........................................11 2.5.2. Balanced Routing protocols ...................................13 Chapter III: System Modeling .......................................... 15 3.1. System Models .........................................................15 3.2. RPL Objective Function: ............................................17 Chapter IV: SB-RPL Design .............................................. 20 4.1. Topology-Aware Node Influence ...............................20 4.2. RPL Control Message DIO extension in support of balancing routing .............................................................20 4.3. SB-RPL Design ...........................................................21 Chapter V: Evaluation ...................................................... 25 5.1. RPL in Contiki OS .......................................................25 5.2. Methodology .............................................................26 5.2.1. Testbed Experiments: ..............................................26 5.3. Compared Objective Functions ...................................28 5.4. Metrics........................................................................29 5.5. Testbed Experiments....................................................30 5.5.1. Impact of α and β: ....................................................30 5.5.2. Objective Function Comparison ...............................36 5.6. Cooja-based Simulations ............................................38 5.6.1. Impact of Network Scales ........................................40 5.6.2. Impact of Network Density ......................................41 Chapter VI: Conclusion ..................................................... 43 Bibliography ..................................................................... 44 요 약.................................................................................. 50 Acknowledgments ............................................................ 52Maste