Traffic-differentiation-based modular QoS localized routing for wireless sensor networks

A new localized quality of service (QoS) routing protocol for wireless sensor networks (WSN) is proposed in this paper. The proposed protocol targets WSN's applications having different types of data traffic. It is based on differentiating QoS requirements according to the data type, which enables to provide several and customized QoS metrics for each traffic category. With each packet, the protocol attempts to fulfill the required data-related QoS metric(s) while considering power efficiency. It is modular and uses geographical information, which eliminates the need of propagating routing information. For link quality estimation, the protocol employs distributed, memory and computation efficient mechanisms. It uses a multisink single-path approach to increase reliability. To our knowledge, this protocol is the first that makes use of the diversity in data traffic while considering latency, reliability, residual energy in sensor nodes, and transmission power between nodes to cast QoS metrics as a multiobjective problem. The proposed protocol can operate with any medium access control (MAC) protocol, provided that it employs an acknowledgment (ACK) mechanism. Extensive simulation study with scenarios of 900 nodes shows the proposed protocol outperforms all comparable state-of-the-art QoS and localized routing protocols. Moreover, the protocol has been implemented on sensor motes and tested in a sensor network testbed

Unified Theory of Relativistic Identification of Information in a Systems Age: Proposed Convergence of Unique Identification with Syntax and Semantics through Internet Protocol version 6

Unique identification of objects are helpful to the decision making process in many domains. Decisions, however, are often based on information that takes into account multiple factors. Physical objects and their unique identification may be one of many factors. In real-world scenarios, increasingly decisions are based on collective information gathered from multiple sources (or systems) and then combined to a higher level domain that may trigger a decision or action. Currently, we do not have a globally unique mechanism to identify information derived from data originating from objects and processes. Unique identification of information, hence, is an open question. In addition, information, to be of value, must be related to the context of the process. In general, contextual information is of greater relevance in the decision making process or in decision support systems. In this working paper, I shall refer to such information as decisionable information. The suggestion here is to utilize the vast potential of internet protocol version six (IPv6) to uniquely identify not only objects and processes but also relationships (semantics) and interfaces (sensors). Convergence of identification of diverse entities using the globally agreed structure of IPv6 offers the potential to identify 3.4x10[subscript 38] instances based on the fact that the 128-bit IPv6 structure can support 3.4x10[subscript 38] unique addresses. It is not necessary that all instances must be connected to the internet or routed or transmitted simply because an IP addressing scheme is suggested. This is a means for identification that will be globally unique and offers the potential to be connected or routed via the internet. In this working paper, scenarios offer [1] new revenue potential from data routing (P2P traffic track and trace) for telecommunication industries, [2] potential for use in healthcare and biomedical community, [3] scope of use in the semantic web structure by transitioning URIs used in RDF, [4] applications involving thousands of mobile ad hoc sensors (MANET) that demand dynamic adaptive auto-reconfiguration. This paper presents a confluence of ideas

Design of Wireless Communication Networks for Cyber-Physical Systems with Application to Smart Grid

Cyber-Physical Systems (CPS) are the next generation of engineered systems in which computing, communication, and control technologies are tightly integrated. On one hand, CPS are generally large with components spatially distributed in physical world that has lots of dynamics; on the other hand, CPS are connected, and must be robust and responsive. Smart electric grid, smart transportation system are examples of emerging CPS that have significant and far-reaching impact on our daily life. In this dissertation, we design wireless communication system for CPS. To make CPS robust and responsive, it is critical to have a communication subsystem that is reliable, adaptive, and scalable. Our design uses a layered structure, which includes physical layer, multiple access layer, network layer, and application layer. Emphases are placed on multiple access and network layer. At multiple access layer, we have designed three approaches, namely compressed multiple access, sample-contention multiple access, and prioritized multiple access, for reliable and selective multiple access. At network layer, we focus on the problem of creating reliable route, with service interruption anticipated. We propose two methods: the first method is a centralized one that creates backup path around zones posing high interruption risk; the other method is a distributed one that utilizes Ant Colony Optimization (ACO) and positive feedback, and is able to update multipath dynamically. Applications are treated as subscribers to the data service provided by the communication system. Their data quality requirements and Quality of Service (QoS) feedback are incorporated into cross-layer optimization in our design. We have evaluated our design through both simulation and testbed. Our design demonstrates desired reliability, scalability and timeliness in data transmission. Performance gain is observed over conventional approaches as such random access

A Framework for Service Differentiation and Optimization in Multi-hop Wireless Networks

In resource-constrained networks such as multi-hop wireless networks (MHWNs), service differentiation algorithms designed to address end users' interests (e.g. user satisfaction, QoS, etc.) should also consider efficient utilization of the scarce network resources in order to maximize the network's interests (e.g. revenue). For this very reason, service differentiation in MHWNs is quite different from the wired network scenario. We propose a service differentiation tool called the ``Investment Function'', which essentially captures the network's cumulative resource investment in a given packet at a given time. This investment value can be used by the network algorithm to implement specific service differentiation principles. As proof-of-concept, we use the investment function to improve fairness among simultaneous flows that traverse varying number of hops in a MHWN (multihop flow fairness). However, to attain the optimal value of a specific service differentiation objective, optimal service differentiation and investment function parameters may need to be computed. The optimal parameters can be computed by casting the service differentiation problem as a network flow problem in MHWNs, with the goal of optimizing the service differentiation objective. The capacity constraints for these problems require knowledge of the adjacent-node interference values, and constructing these constraints could be very expensive based on the transmission scheduling scheme used. As a result, even formulating the optimization problem may take unacceptable computational effort or memory or both. Under optimal scheduling, the adjacent node interference values (and thus the capacity constraints) are not only very expensive to compute, but also cannot be expressed in polynomial form. Therefore, existing optimization techniques cannot be directly applied to solve optimization problems in MHWNs. To develop an efficient optimization framework, we first model the MHWN as a Unit Disk Graph (UDG). The optimal transmission schedule in the MHWN is related to the chromatic number of the UDG, which is very expensive to compute. However, the clique number, which is a lower bound on the chromatic number, can be computed in polynomial time in UDGs. Through an empirical study, we obtain tighter bounds on the ratio of the chromatic number to clique number in UDGs, which enables us to leverage existing polynomial time clique-discovery algorithms to compute very close approximations to the chromatic number value. This approximation not only allows us to quickly formulate the capacity constraints in polynomial form, but also allows us to significantly deviate from the traditional approach of discovering all or most of the constraints \textit{a priori}; instead, we can discover the constraints as needed. We have integrated this approach of constraint-discovery into an active-set optimization algorithm (Gradient Projection method) to solve network flow problems in multi-hop wireless networks. Our results show significant memory and computational savings when compared to existing methods

A Multichannel Medium Access Control and its Performance Estimation for Multihop Wireless Sensor Networks

The thesis proposes a three-tier architecture wireless sensor network to monitor the environment of wide rural area. To enhance the network throughput, a multichannel MAC, 2HCR, is developed. The performance of 2HCR is examined for both single and bidirectional traffics. For the bidirectional traffic, a simple priority support scheme is proposed to give a priority for command traffic. Also, a procedure to estimate the throughput of multihop networks is developed to be used in network design

Telemedicine

Telemedicine is a rapidly evolving field as new technologies are implemented for example for the development of wireless sensors, quality data transmission. Using the Internet applications such as counseling, clinical consultation support and home care monitoring and management are more and more realized, which improves access to high level medical care in underserved areas. The 23 chapters of this book present manifold examples of telemedicine treating both theoretical and practical foundations and application scenarios

Optimal resource scheduling for energy-efficient next generation wireless networks

Cellular networks can provide highly available and reliable communication links to the Internet of Things (IoT) applications, letting the connected Things paradigm gain much more momentum than ever. Also, the rich information collected from the Things with sensing capabilities can guide the network operator to an unforeseen direction, allowing the underlying cellular networks to be further optimized. In this regard, the cellular networks and IoT are conceived as the key components of the beyond-4G and future 5G networks. Therefore, in this dissertation, we study each of the two components in depth, focusing on how to optimize the networking resources for the quality service and better energy-efficiency. To begin with, we study the heterogeneous cellular network architecture which is a major enhancement to the current 4G network by means of the base station (BS) densification and traffic offloading. In particular, the densely deployed short-range, low-power smallcell base stations (SBSs) can significantly improve the frequency reuse, throughput performance and the energy-efficiency. We then study the heterogeneous C-RAN (cloud radio access network), which is one of the core enablers of the next generation 5G cellular networks. In particular, with the high availability provided by the long-range macro BS (MBS), the heterogeneous C-RAN (H-CRAN) can effectively enhance the overall resource utilization compared to the conventional C-RANs. In each study, we propose an optimal resource scheduling and service provisioning scheme to provide a quality service to users in a resource-efficient manner. In addition, we carry out two studies for the Internet of Things (IoT) networks operating with the IEEE 802.11ah standard. Specifically, we introduce energy-efficient device management algorithms for the battery-operated, resource-constrained IoT sensor devices to prolong their lifetime by optimally scheduling their activation. The enhanced power saving mechanism and the optimal sensing algorithm that we propose in each study can effectively improve both the energy-efficiency of the IoT devices and the lifetime of the entire network

Network Resilience Architecture and Analysis for Smart Homes

The Internet of Things (IoT) is evolving rapidly to every aspect of human life including, healthcare, homes, cities, and driverless vehicles that makes humans more dependent on the Internet and related infrastructure. While many researchers have studied the structure of the Internet that is resilient as a whole, new studies are required to investigate the resilience of the edge networks in which people and “things” connect to the Internet. Since the range of service requirements varies at the edge of the network, a wide variety of technologies with different topologies are involved. Though the heterogeneity of the technologies at the edge networks can improve the robustness through the diversity of mechanisms, other issues such as connectivity among the utilized technologies and cascade of failures would not have the same effect as a simple network. Therefore, regardless of the size of networks at the edge, the structure of these networks is complicated and requires appropriate study. In this dissertation, we propose an abstract model for smart homes, as part of one of the fast-growing networks at the edge, to illustrate the heterogeneity and complexity of the network structure. As the next step, we make two instances of the abstract smart home model and perform a graph-theoretic analysis to recognize the fundamental behavior of the network to improve its robustness. During the process, we introduce a formal multilayer graph model to highlight the structures, topologies, and connectivity of various technologies at the edge networks and their connections to the Internet core. Furthermore, we propose another graph model, technology interdependence graph, to represent the connectivity of technologies. This representation shows the degree of connectivity among technologies and illustrates which technologies are more vulnerable to link and node failures. Moreover, the dominant topologies at the edge change the node and link vulnerability, which can be used to apply worst-case scenario attacks. Restructuring of the network by adding new links associated with various protocols to maximize the robustness of a given network can have distinctive outcomes for different robustness metrics. However, typical centrality metrics usually fail to identify important nodes in multi-technology networks such as smart homes. We propose four new centrality metrics to improve the process of identifying important nodes in multi-technology networks and recognize vulnerable nodes. We perform the process of improvement through modifying topology, adding extra nodes, and links when necessary. The improvement process would be verified by calculation of the proper graph metrics and introducing new metrics when it is appropriate. Finally, we study over 1000 different smart home topologies to examine the resilience of the networks with typical and the proposed centrality metrics