An energy efficient routing scheme by using GPS information for wireless sensor networks

In the process of transmission in wireless sensor networks (WSN), a vital problem is that a centre region close to the sink will form in which sensors have to cost vast amount of energy. To communicate in an energy-efficient manner, compressed sensing (CS) has been employed gradually. However, the performance of plain CS is significantly dependant on the specific data gathering strategy in practice. In this paper, we propose an energy-efficient data gathering scheme based on regionalisation CS. Subsequently, advanced methods for practical applications are considered. Experiments reveal that our scheme outperforms distributed CS, the straight forward and the mixed schemes by comparing different parameters of the data package, and the considered methods also guarantee its feasibility.N/

W-GUN: Whale Optimization for Energy and Delay centric Green Underwater Networks

Underwater Sensor Networks (UWSNs) has witnessed significant R&D attention in both academia and industries due to its growing application domain such as border security, freight via sea or river, natural petroleum production, etc. Considering the deep underwater oriented access constraints, energy centric communication for lifetime maximization of tiny sensor nodes in UWSNs is one of the key research themes in this domain. Existing literature on green UWSNs are majorly adapted from the existing techniques in traditional wireless sensor network without giving much attention to the realistic impact of underwater network environments resulting in degraded performance. Towards this end, this paper presents an adapted whale optimization algorithm-based energy and delay centric green UWSNs framework (W-GUN). It focuses on exploiting dynamic underwater network characteristics by effectively utilizing underwater whale centric optimization in relay node selection. Firstly, an underwater relay- node optimization model is mathematically derived focusing on whale and wolf optimization algorithms for incorporating realistic underwater characteristics. Secondly, the optimization model is used to develop an adapted whale and grey wolf optimization algorithm. Thirdly, a complete work-flow of the W-GUN framework is presented with the optimization flowchart. The comparative performance evaluation attests the benefits of the proposed framework as compared to the state-of-the-art techniques considering various metrics related to underwater network environments

FracBot: Design of wireless underground sensor networks for mapping hydraulic fractures and determining reservoir parameters in unconventional systems

Wireless underground sensor networks (WUSNs) enable a wide variety of emerging applications that are not possible with current underground monitoring techniques, which require miniaturized wireless sensor systems for mapping hydraulic fractures, monitoring unconventional reservoirs and measuring other wellbore parameters. We call these devices FracBots (Fracture Robots), an extension of RFID (Radio Frequency IDentifcation) tags that realize WUSNs for mapping and characterization of hydraulic fractures in unconventional reservoirs. The objective of this thesis is to design fully integrated magnetic induction (MI)-based FracBots (WUSNs) that enable reliable and e fficient wireless communications in underground oil reservoirs for performing the in-situ monitoring of oil reservoirs. This is very crucial for determining the sweet spot of oil and natural gas reserves. To this end, we have contributed in four areas as follows: fi rst, we develop a novel cross-layer communication framework for MI-based FracBot networks in dynamically changing underground environments. The framework combines a joint selection of modulation, channel coding, power control and a geographic forwarding paradigm. Second, we develop a novel MI-based localization framework that exploits the unique properties of MI- eld to determine the locations of the randomly deployed FracBot nodes in oil reservoirs. Third, we develop an accurate energy framework of a linear FracBot network topology that generates feasible nodes' transmission rates and network topology while always guaranteeing su fficient energy. Then, we design, develop, and fabricate MI-based FracBot nodes. Finally, to validate the performance of our solutions in our produced prototype of FracBot nodes, we develop a physical MI-based WUSN testbed.Ph.D

Use of Inferential Statistics to Design Effective Communication Protocols for Wireless Sensor Networks

This thesis explores the issues and techniques associated with employing the principles of inferential statistics to design effective Medium Access Control (MAC), routing and duty cycle management strategies for multihop Wireless Sensor Networks (WSNs). The main objective of these protocols are to maximise the throughput of the network, to prolong the lifetime of nodes and to reduce the end-to-end delay of packets over a general network scenario without particular considerations for specific topology configurations, traffic patterns or routing policies. WSNs represent one of the leading-edge technologies that have received substantial research efforts due to their prominent roles in many applications. However, to design effective communication protocols for WSNs is particularly challenging due to the scarce resources of these networks and the requirement for large-scale deployment. The MAC, routing and duty cycle management protocols are amongst the important strategies that are required to ensure correct operations of WSNs. This thesis makes use of the inferential statistics field to design these protocols; inferential statistics was selected as it provides a rich design space with powerful approaches and methods. The MAC protocol proposed in this thesis exploits the statistical characteristics of the Gamma distribution to enable each node to adjust its contention parameters dynamically based on its inference for the channel occupancy. This technique reduces the service time of packets and leverages the throughput by improving the channel utilisation. Reducing the service time minimises the energy consumed in contention to access the channel which in turn prolongs the lifetime of nodes. The proposed duty cycle management scheme uses non-parametric Bayesian inference to enable each node to determine the best times and durations for its sleeping durations without posing overheads on the network. Hence the lifetime of node is prolonged by mitigating the amount of energy wasted in overhearing and idle listening. Prolonging the lifetime of nodes increases the throughput of the network and reduces the end-to-end delay as it allows nodes to route their packets over optimal paths for longer periods. The proposed routing protocol uses one of the state-of-the-art inference techniques dubbed spatial reasoning that enables each node to figure out the spatial relationships between nodes without overwhelming the network with control packets. As a result, the end-to-end delay is reduced while the throughput and lifetime are increased. Besides the proposed protocols, this thesis utilises the analytical aspects of statistics to develop rigorous analytical models that can accurately predict the queuing and medium access delay and energy consumption over multihop networks. Moreover, this thesis provides a broader perspective for design of communication protocols for WSNs by casting the operations of these networks in the domains of the artificial chemistry discipline and the harmony search optimisation algorithm

DYNAMIC ROUTING WITH CROSS-LAYER ADAPTATIONS FOR MULTI-HOP WIRELESS NETWORKS

In recent years there has been a proliferation of research on a number of wireless multi-hop networks that include mobile ad-hoc networks, wireless mesh networks, and wireless sensor networks (WSNs). Routing protocols in such networks are of- ten required to meet design objectives that include a combination of factors such as throughput, delay, energy consumption, network lifetime etc. In addition, many mod- ern wireless networks are equipped with multi-channel radios, where channel selection plays an important role in achieving the same design objectives. Consequently, ad- dressing the routing problem together with cross-layer adaptations such as channel selection is an important issue in such networks. In this work, we study the joint routing and channel selection problem that spans two domains of wireless networks. The first is a cost-effective and scalable wireless-optical access networks which is a combination of high-capacity optical access and unethered wireless access. The joint routing and channel selection problem in this case is addressed under an anycasting paradigm. In addition, we address two other problems in the context of wireless- optical access networks. The first is on optimal gateway placement and network planning for serving a given set of users. And the second is the development of an analytical model to evaluate the performance of the IEEE 802.11 DCF in radio-over- fiber wireless LANs. The second domain involves resource constrained WSNs where we focus on route and channel selection for network lifetime maximization. Here, the problem is further exacerbated by distributed power control, that introduces addi- tional design considerations. Both problems involve cross-layer adaptations that must be solved together with routing. Finally, we present an analytical model for lifetime calculation in multi-channel, asynchronous WSNs under optimal power control