Design of implicit routing protocols for large scale mobile wireless sensor networks

Strathclyde theses - ask staff. Thesis no. : T13189Most developments in wireless sensor networks (WSNs) routing protocols address static network scenarios. Schemes developed to manage mobility in other mobile networking implementations do not translate effectively to WSNs as the system design parameters are markedly different. Thus this research focuses on the issues of mobility and scalability in order to enable the full potential of WSNs to self-organise and co-operate and in so doing, meet the requirements of a rich mix of applications. In the goal of designing efficient, reliable routing protocols for large scale mobile WSN applications, this work lays the foundation by firstly presenting a strong case supported by extensive simulations, for the use of implicit connections. Then two novel implicit routing protocols - Virtual Grid Paging (VGP) and Virtual Zone Registration and Paging (VZRP) - that treat packet routing from node mobility and network scalability viewpoints are designed and analysed. Implicit routing exploits the connection availability and diversity in the underlying network to provide benefits such as fault tolerance, overhead control and improvement in QoS (Quality of Service) such as delay. Analysis and simulation results show that the proposed protocols guarantee significant improvement, delivering a more reliable, more efficient and better network performance compared with alternatives.Most developments in wireless sensor networks (WSNs) routing protocols address static network scenarios. Schemes developed to manage mobility in other mobile networking implementations do not translate effectively to WSNs as the system design parameters are markedly different. Thus this research focuses on the issues of mobility and scalability in order to enable the full potential of WSNs to self-organise and co-operate and in so doing, meet the requirements of a rich mix of applications. In the goal of designing efficient, reliable routing protocols for large scale mobile WSN applications, this work lays the foundation by firstly presenting a strong case supported by extensive simulations, for the use of implicit connections. Then two novel implicit routing protocols - Virtual Grid Paging (VGP) and Virtual Zone Registration and Paging (VZRP) - that treat packet routing from node mobility and network scalability viewpoints are designed and analysed. Implicit routing exploits the connection availability and diversity in the underlying network to provide benefits such as fault tolerance, overhead control and improvement in QoS (Quality of Service) such as delay. Analysis and simulation results show that the proposed protocols guarantee significant improvement, delivering a more reliable, more efficient and better network performance compared with alternatives

STEAR: Secure Trust-Aware Energy-Efficient Adaptive Routing in Wireless Sensor Networks

Secure communication is one of the most critical challenging tasks in multi-hop Wireless Sensor Networks (WSNs). Routing protocols of WSNs are highly susceptible to various attacks, which replay the routing information through the malicious node and steal the identities of the valid nodes in a network. The malicious nodes forward the packets far away from the sink, increasing the packet drop ratio, that sluggishes overall network efficiency. In order to overcome this problem, we have designed and implemented a secure trust aware energy efficient adaptive routing (STEAR) for dynamic WSNs. This protocol provides secure, trustworthy and energy efficient routing for multihop networks. STEAR is designed with effective mechanisms to identify the malicious nodes using dynamic secret key (DSK) assignment, trust and energy monitoring, and packets flow status monitoring. Simulation results show that network efficiency and throughput are better and packet drop ratio is reduced compared to earlier works

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

Dynamic Clustering and Management of Mobile Wireless Sensor Networks

In Wireless Sensor Networks (WSNs), routing data towards the sink leads to unbalanced energy consumption among intermediate nodes resulting in high data loss rate. The use of multiple Mobile Data Collectors (MDCs) has been proposed in the literature to mitigate such problems. MDCs help to achieve uniform energy-consumption across the network, fill coverage gaps, and reduce end-to-end communication delays, amongst others. However, mechanisms to support MDCs such as location advertisement and route maintenance introduce significant overhead in terms of energy consumption and packet delays. In this paper, we propose a self-organizing and adaptive Dynamic Clustering (DCMDC) solution to maintain MDC-relay networks. This solution is based on dividing the network into well-delimited clusters called Service Zones (SZs). Localizing mobility management traffic to a SZ reduces signaling overhead, route setup delay and bandwidth utilization. Network clustering also helps to achieve scalability and load balancing. Smaller network clusters make buffer overflows and energy depletion less of a problem. These performance gains are expected to support achieving higher information completeness and availability as well as maximizing the network lifetime. Moreover, maintaining continuous connectivity between the MDC and sensor nodes increases information availability and validity. Performance experiments show that DCMDC outperforms its rival in the literature. Besides the improved quality of information, the proposed approach improves the packet delivery ratio by up to 10%, end-to-end delay by up to 15%, energy consumption by up to 53%, energy balancing by up to 51%, and prolongs the network lifetime by up to 53%

SecAODV: A Secure healthcare routing scheme based on hybrid cryptography in wireless body sensor networks

In recent decades, the use of sensors has dramatically grown to monitor human body activities and maintain the health status. In this application, routing and secure data transmission are very important to prevent the unauthorized access by attackers to health data. In this article, we propose a secure routing scheme called SecAODV for heterogeneous wireless body sensor networks. SecAODV has three phases: bootstrapping, routing between cluster head nodes, and communication security. In the bootstrapping phase, the base station loads system parameters and encryption functions in the memory of sensor nodes. In the routing phase, each cluster head node calculates its degree based on several parameters, including, distance, residual energy, link quality, and the number of hops, to decide for rebroadcasting the route request (RREQ) message. In the communication security phase, a symmetric cryptography method is used to protect intra-cluster communications. Also, an asymmetric cryptography method is used to secure communication links between cluster head nodes. The proposed secure routing scheme is simulated in the network simulator version 2 (NS2) simulator. The simulation results are compared with the secure multi tier energy-efficient routing scheme (SMEER) and the centralized low-energy adaptive clustering hierarchy (LEACH-C). The results show that SecAODV improves end-to-end delay, throughput, energy consumption, packet delivery rate (PDR), and packet loss rate (PLR)

Stochastic Optimization and Machine Learning Modeling for Wireless Networking

In the last years, the telecommunications industry has seen an increasing interest in the development of advanced solutions that enable communicating nodes to exchange large amounts of data. Indeed, well-known applications such as VoIP, audio streaming, video on demand, real-time surveillance systems, safety vehicular requirements, and remote computing have increased the demand for the efficient generation, utilization, management and communication of larger and larger data quantities. New transmission technologies have been developed to permit more efficient and faster data exchanges, including multiple input multiple output architectures or software defined networking: as an example, the next generation of mobile communication, known as 5G, is expected to provide data rates of tens of megabits per second for tens of thousands of users and only 1 ms latency. In order to achieve such demanding performance, these systems need to effectively model the considerable level of uncertainty related to fading transmission channels, interference, or the presence of noise in the data. In this thesis, we will present how different approaches can be adopted to model these kinds of scenarios, focusing on wireless networking applications. In particular, the first part of this work will show how stochastic optimization models can be exploited to design energy management policies for wireless sensor networks. Traditionally, transmission policies are designed to reduce the total amount of energy drawn from the batteries of the devices; here, we consider energy harvesting wireless sensor networks, in which each device is able to scavenge energy from the environment and charge its battery with it. In this case, the goal of the optimal transmission policies is to efficiently manage the energy harvested from the environment, avoiding both energy outage (i.e., no residual energy in a battery) and energy overflow (i.e., the impossibility to store scavenged energy when the battery is already full). In the second part of this work, we will explore the adoption of machine learning techniques to tackle a number of common wireless networking problems. These algorithms are able to learn from and make predictions on data, avoiding the need to follow limited static program instructions: models are built from sample inputs, thus allowing for data-driven predictions and decisions. In particular, we will first design an on-the-fly prediction algorithm for the expected time of arrival related to WiFi transmissions. This predictor only exploits those network parameters available at each receiving node and does not require additional knowledge from the transmitter, hence it can be deployed without modifying existing standard transmission protocols. Secondly, we will investigate the usage of particular neural network instances known as autoencoders for the compression of biosignals, such as electrocardiography and photo plethysmographic sequences. A lightweight lossy compressor will be designed, able to be deployed in wearable battery-equipped devices with limited computational power. Thirdly, we will propose a predictor for the long-term channel gain in a wireless network. Differently from other works in the literature, such predictor will only exploit past channel samples, without resorting to additional information such as GPS data. An accurate estimation of this gain would enable to, e.g., efficiently allocate resources and foretell future handover procedures. Finally, although not strictly related to wireless networking scenarios, we will show how deep learning techniques can be applied to the field of autonomous driving. This final section will deal with state-of-the-art machine learning solutions, proving how these techniques are able to considerably overcome the performance given by traditional approaches

Multi-Objective Energy Efficient Adaptive Whale Optimization Based Routing for Wireless Sensor Network

In Wireless Sensor Networks (WSNs), routing algorithms can provide energy efficiency. However, due to unbalanced energy consumption for all nodes, the network lifetime is still prone to degradation. Hence, energy efficient routing was developed in this article by selecting cluster heads (CH) with the help of adaptive whale optimization (AWOA) which was used to reduce time-consumption delays. The multi-objective function was developed for CH selection. The clusters were then created using the distance function. After establishing groupings, the supercluster head (SCH) was selected using the benefit of a fuzzy inference system (FIS) which was used to collect data for all CHs and send them to the base station (BS). Finally, for the data-transfer procedure, hop count routing was used. An Oppositional-based Whale optimization algorithm (OWOA) was developed for multi-constrained QoS routing with the help of AWOA. The performance of the proposed OWOA methodology was analyzed according to the following metrics: delay, delivery ratio, energy, NLT, and throughput and compared with conventional techniques such as particle swarm optimization, genetic algorithm, and Whale optimization algorithm