Energy-efficient multi-criteria packet forwarding in multi-hop wireless networks

Reliable multi-hop packet forwarding is an important requirement for the implementation of realistic large-scale wireless ad-hoc networks. However, packet forwarding methods based on a single criterion, such as the traditional greedy geographic forwarding, are not sufficient in most realistic wireless settings because perfect-reception-within-rangecannot be assumed. Furthermore, methods where the selection of intermediate relaying nodes is performed at the transmitter-side do not adapt well to rapidly changing network environments. Although a few link-aware geographic forwarding schemes have been reported in the literature, the tradeoffs between multiple decision criteria and their impact on network metrics such as throughput, delay and energy consumption have not been studied. This dissertation presents a series of strategies aimed at addressing the challenges faced by the choice of relay nodes in error-prone dynamic wireless network environments. First, a single-criterion receiver-side relay election (RSRE) is introduced as a distributed alternative to the traditional transmitter-side relay selection. Contrary to the transmitter- side selection, at each hop, an optimal node is elected among receivers to relay packets toward the destination. Next, a multi-criteria RSRE, which factors multiple decision criteria in the election process at lower overhead cost, is proposed. A general cost metric in the form of a multi-parameter mapping function aggregates decision criteria into a single metric used to rank potential relay candidates. A two-criteria RSRE case study shows that a proper combination of greedy forwarding and link quality leads to higher energy efficiency and substantial improvement in the end-to-end delay. Last, mesh multi-path forwarding methods are examined. A generalized mesh construction algorithm in introduced to show impact of a mesh structure on network performance

Geographical area network-structural health monitoring utility computing model

In view of intensified disasters and fatalities caused by natural phenomena and geographical expansion, there is a pressing need for a more effective environment logging for a better management and urban planning. This paper proposes a novel utility computing model (UCM) for structural health monitoring (SHM) that would enable dynamic planning of monitoring systems in an efficient and cost-effective manner in form of a SHM geo-informatics system. The proposed UCM consists of networked SHM systems that send geometrical SHM variables to SHM-UCM gateways. Every gateway is routing the data to SHM-UCM servers running a geo-spatial patch health assessment and prediction algorithm. The inputs of the prediction algorithm are geometrical variables, environmental variables, and payloads. The proposed SHM-UCM is unique in terms of its capability to manage heterogeneous SHM resources. This has been tested in a case study on Qatar University (QU) in Doha Qatar, where it looked at where SHM nodes are distributed along with occupancy density in each building. This information was taken from QU routers and zone calculation models and were then compared to ideal SHM system data. Results show the effectiveness of the proposed model in logging and dynamically planning SHM.This publication was made possible by NPRP grant # 8-1781-2-725 from the Qatar National Research Fund (a member of Qatar Foundation). The publication of this article was funded by the Qatar National Library

A MULTI AWARE MULTI-LEVELS HETEROGENOUS ROUTING (MAMHR) PROTOCOL FOR WIRELESS SENSOR NETWORKS

Wireless Sensor Networks (WSNs) are generally set out in a remote workplace, since the sensor nodes are tiny in size, cost-proficient, low-power gadgets, and have restricted battery supply. Due to the constrained availability of power sources, energy utilization has been considered as the most crucial factor for proposing network routing protocols. The fundamental concern is to improve the lifetime of the network based on the energy constraints. Several homogenous cluster-based routing protocols have been proposed in literature for lifetime improvement of the sensor network but many of them fail to function effectively in heterogeneous environment and moreover, these protocols have not considered any other awareness parameters than lifetime and energy consumption. In this work, a Multi Aware Multi-Levels Heterogeneous Routing (MAMHR) protocol, focusing on the principle of multi-level heterogeneity by considering multiple awareness parameters of the network such as Quality of Service (QoS), shortest path estimation and suitable localization technique, is proposed. Scoped Bellman Ford Routing (SBFR) algorithm is used for shortest path estimation, Reliability, Availability, and Serviceability (RAS) factors are considered for QoS awareness and Time Difference of Arrival (TDOA) technique is used for location estimation. Lifetime awareness parameters of the proposed scheme were compared with already existing prominent protocols LEACH, SEP and ZSEP and a significant improvement in lifetime of entire network was obtained. Simulation results corresponding to the respective multiple awareness parameters also shows that these parameters can be incorporated into the selected heterogeneous environment without affecting the energy consumption constraints of the proposed scheme

A Virtual Infrastructure for Mitigating Typical Challenges in Sensor Networks

Sensor networks have their own distinguishing characteristics that set them apart from other types of networks. Typically, the sensors are deployed in large numbers and in random fashion and the resulting sensor network is expected to self-organize in support of the mission for which it was deployed. Because of the random deployment of sensors that are often scattered from an overflying aircraft, the resulting network is not easy to manage since the sensors do not know their location, do not know how to aggregate their sensory data and where and how to route the aggregated data. The limited energy budget available to sensors makes things much worse. To save their energy, sensors have to sleep and wake up asynchronously. However, while promoting energy awareness, these actions continually change the underlying network topology and make the basic network protocols more complex. Several techniques have been proposed in different areas of sensor networks. Most of these techniques attempt to solve one problem in isolation from the others, hence protocol designers have to face the same common challenges again and again. This, in turn, has a direct impact on the complexity of the proposed protocols and on energy consumption. Instead of using this approach we propose to construct a lightweight backbone that can help mitigate many of the typical challenges in sensor networks and allow the development of simpler network protocols. Our backbone construction protocol starts by tiling the area around each sink using identical regular hexagons. After that, the closest sensor to the center of each of these hexagons is determined—we refer to these sensors as backbone sensors. We define a ternary coordinate system to refer to hexagons. The resulting system provides a complete set of communication paths that can be used by any geographic routing technique to simplify data communication across the network. We show how the constructed backbone can help mitigate many of the typical challenges inherent to sensor networks. In addition to sensor localization, the network backbone provides an implicit clustering mechanism in which each hexagon represents a cluster mud the backbone sensor around its center represents the cluster head. As cluster heads, backbone sensors can be used to coordinate task assignment, workforce selection, and data aggregation for different sensing tasks. They also can be used to locally synchronize and adjust the duty cycle of non-backbone sensors in their neighborhood. Finally, we propose “Backbone Switching”, a technique that creates alternative backbones and periodically switches between them in order to balance energy consumption among sensors by distributing the additional load of being part of the backbone over larger number of sensors