Performance Modelling and Optimisation of Multi-hop Networks

A major challenge in the design of large-scale networks is to predict and optimise the total time and energy consumption required to deliver a packet from a source node to a destination node. Examples of such complex networks include wireless ad hoc and sensor networks which need to deal with the effects of node mobility, routing inaccuracies, higher packet loss rates, limited or time-varying effective bandwidth, energy constraints, and the computational limitations of the nodes. They also include more reliable communication environments, such as wired networks, that are susceptible to random failures, security threats and malicious behaviours which compromise their quality of service (QoS) guarantees. In such networks, packets traverse a number of hops that cannot be determined in advance and encounter non-homogeneous network conditions that have been largely ignored in the literature. This thesis examines analytical properties of packet travel in large networks and investigates the implications of some packet coding techniques on both QoS and resource utilisation. Specifically, we use a mixed jump and diffusion model to represent packet traversal through large networks. The model accounts for network non-homogeneity regarding routing and the loss rate that a packet experiences as it passes successive segments of a source to destination route. A mixed analytical-numerical method is developed to compute the average packet travel time and the energy it consumes. The model is able to capture the effects of increased loss rate in areas remote from the source and destination, variable rate of advancement towards destination over the route, as well as of defending against malicious packets within a certain distance from the destination. We then consider sending multiple coded packets that follow independent paths to the destination node so as to mitigate the effects of losses and routing inaccuracies. We study a homogeneous medium and obtain the time-dependent properties of the packet’s travel process, allowing us to compare the merits and limitations of coding, both in terms of delivery times and energy efficiency. Finally, we propose models that can assist in the analysis and optimisation of the performance of inter-flow network coding (NC). We analyse two queueing models for a router that carries out NC, in addition to its standard packet routing function. The approach is extended to the study of multiple hops, which leads to an optimisation problem that characterises the optimal time that packets should be held back in a router, waiting for coding opportunities to arise, so that the total packet end-to-end delay is minimised

Configuring heterogeneous wireless sensor networks under quality-of-service constraints

Wireless sensor networks (WSNs) are useful for a diversity of applications, such as structural monitoring of buildings, farming, assistance in rescue operations, in-home entertainment systems or to monitor people's health. A WSN is a large collection of small sensor devices that provide a detailed view on all sides of the area or object one is interested in. A large variety of WSN hardware platforms is readily available these days. Many operating systems and protocols exist to support essential functionality such as communication, power management, data fusion, localisation, and much more. A typical sensor node has a number of settings that affect its behaviour and the function of the network itself, such as the transmission power of its radio and the number of measurements taken by its sensor per minute. As the number of nodes in a WSN may be very large, the collection of independent parameters in these networks – the configuration space – tends to be enormous. The user of the WSN would have certain expectations on the Quality of Service (QoS) of the network. A WSN is deployed for a specific purpose, and has a number of measurable properties that indicate how well the network's task is being performed. Examples of such quality metrics are the time needed for measured information to reach the user, the degree of coverage of the area, or the lifetime of the network. Each point in the configuration space of the network gives rise to a certain value in each of the quality metrics. The user may place constraints on the quality metrics, and wishes to optimise the configuration to meet their goals. Work on sensor networks often focuses on optimising only one metric at the time, ignoring the fact that improving one aspect of the system may deteriorate other important performance characteristics. The study of trade-offs between multiple quality metrics, and a method to optimally configure a WSN for several objectives simultaneously – until now a rather unexplored field – is the main contribution of this thesis. There are many steps involved in the realisation of a WSN that is fulfilling a task as desired. First of all, the task needs to be defined and specified, and appropriate hardware (sensor nodes) needs to be selected. After that, the network needs to be deployed and properly configured. This thesis deals with the configuration problem, starting with a possibly heterogeneous collection of nodes distributed in an area of interest, suitable models of the nodes and their interaction, and a set of task-level requirements in terms of quality metrics. We target the class of WSNs with a single data sink that use a routing tree for communication. We introduce two models of tasks running on a sensor network – target tracking and spatial mapping – which are used in the experiments in this thesis. The configuration process is split in a number of phases. After an initialisation phase to collect information about the network, the routing tree is formed in the second configuration phase. We explore the trade-off between two attributes of a tree: the average path length and the maximum node degree. These properties do not only affect the quality metrics, but also the complexity of the remaining optimisation trajectory. We introduce new algorithms to efficiently construct a shortest-path spanning tree in which all nodes have a degree not higher than a given target value. The next phase represents the core of the configuration method: it features a QoS optimiser that determines the Pareto-optimal configurations of the network given the routing tree. A configuration contains settings for the parameters of all nodes in the network, plus the metric values they give rise to. The Pareto-optimal configurations, also known as Pareto points, represent the best possible trade-offs between the quality metrics. Given the vastness of the configuration space, which is exponential in the size of the network, it is impossible to use a brute-force approach and try all possibilities. Still our method efficiently finds all Pareto points, by incrementally searching the configuration space, and discarding potential solutions immediately when they appear to be not Pareto optimal. An important condition for this to work is the ability to compute quality metrics for a group of nodes from the quality metrics of smaller groups of nodes. The precise requirements are derived and shown to hold for the example tasks. Experimental results show that the practical complexity of this algorithm is approximately linear in the number of nodes in the network, and thus scalable to very large networks. After computing the set of Pareto points, a configuration that satisfies the QoS constraints is selected, and the nodes are configured accordingly (the selection and loading phases). The configuration process can be executed in either a centralised or a distributed way. Centralised means that all computations are carried out on a central node, while the distributed algorithms do all the work on the sensor nodes themselves. Simulations show run times in the order of seconds for the centralised configuration of WSNs of hundreds of TelosB sensor nodes. The distributed algorithms take in the order of minutes for the same networks, but have a lower communication overhead. Hence, both approaches have their own pros and cons, and even a combination is possible in which the heavy work is performed by dedicated compute nodes spread across the network. Besides the trade-offs between quality metrics, there is a meta trade-off between the quality and the cost of the configuration process itself. A speed-up of the configuration process can be achieved in exchange for a reduction in the quality of the solutions. We provide complexity-control functionality to fine-tune this quality/cost trade-off. The methods described thus far configure a WSN given a fixed state (node locations, environmental conditions). WSNs, however, are notoriously dynamic during operation: nodes may move or run out of battery, channel conditions may fluctuate, or the demands from the user may change. The final part of this thesis describes methods to adapt the configuration to such dynamism at run time. Especially the case of a mobile sink is treated in detail. As frequently doing global reconfigurations would likely be too slow and too expensive, we use localised algorithms to maintain the routing tree and reconfigure the node parameters. Again, we are able to control the quality/cost trade-off, this time by adjusting the size of the locality in which the reconfiguration takes place. To conclude the thesis, a case study is presented, which highlights the use of the configuration method on a more complex example containing a lot of heterogeneity

Quality-of-service provisioning for dynamic heterogeneous wireless sensor networks

A Wireless Sensor Network (WSN) consists of a large collection of spatially dis- tributed autonomous devices with sensors to monitor physical or environmental conditions, such as air-pollution, temperature and traffic flow. By cooperatively processing and communicating information to central locations, appropriate ac- tions can be performed in response. WSNs perform a large variety of applications, such as the monitoring of elderly persons or conditions in a greenhouse. To correctly and efficiently perform a task, the behaviour of the WSN should be such that sufficient Quality-of-Service (QoS) is provided. QoS is defined by constraints and objectives on network quality metrics, such as a maximum end- to-end packet loss or minimum network lifetime. After defining the application we want the WSN to perform, many steps are involved in designing the WSN such that sufficient QoS is provided. First, a (heterogeneous) set of sensor nodes and protocols need to be selected. Furthermore, a suitable deployment has to be found and the network should be configured for its first use. This configuration involves setting all controllable parameters that influence its behaviour, such as selecting the neighbouring node(s) to communicate to and setting the transmission power of its radio, to ensure that the WSN provides the required QoS. Configuring the network is a complex task as the number of parameters and their possible values are large and trade-offs between multiple quality metrics exist. High transmission power may result in a low packet loss to a neighbouring node, but also in a high power consumption and low lifetime. Heterogeneity in the network causes the impact of parameters to be different between nodes, requiring parameters of nodes to be set individually. Moreover, a static configuration is typically not sufficient to make the most efficient trade-off between the quality metrics at all times in a dynamic environment. Run-time mechanisms are needed to maintain the required level of QoS under changing circumstances, such as changing external interference, mobility of nodes or fluctuating traffic load. This thesis deals with run-time reconfiguration of dynamic heterogeneous wire- less sensor networks to maintain a required QoS, given a deployed network with selected communication protocols and their controllable parameters. The main contribution of this thesis is an efficient QoS provisioning strategy. It consists of three parts: a re-active reconfiguration method, a generic distributed service to estimate network metrics and a pro-active reconfiguration method. In the re-active method, nodes collaboratively respond to discrepancies be- tween the current and required QoS. Nodes use feedback control which, at a given speed, adapts parameters of the node to continuously reduce any error between the locally estimated network QoS and QoS requirements. A dynamic predictive model is used and updated at run-time, to predict how different parameter adap- tations influence the QoS. Setting the speed of adaptation allows us to influence the trade-off between responsiveness and overhead of the approach, and to tune it to the characteristics of the application scenario. Simulations and experiments with an actual deployment show the successful integration in practical scenar- ios. Compared to existing configuration strategies, we are able to extend network lifetime significantly, while maintaining required packet delivery ratios. To solve the non-trivial problem of efficiently estimating network quality met- rics, we introduce a generic distributed service to distributively compute various network metrics. This service takes into account the possible presence of links with asymmetric quality that may vary over time, by repeated forwarding of informa- tion over multiple hops combined with explicit information validity management. The generic service is instantiated from the definition of a recursive local update function that converges to a fixed point representing the desired metric. We show the convergence and stability of various instantiations. Parameters can be set in accordance with the characteristics of the deployment and influence the trade-off between accuracy and overhead. Simulations and experiments show a significant increase in estimation accuracy, and efficiency of a protocol using the estimates, compared to today’s current approaches. This service is integrated in various protocol stacks providing different kinds of network metric estimates. The pro-active reconfiguration method reconfigures in response to predefined run-time detectable events that may cause the network QoS to change signifi- cantly. While the re-active method is generally applicable and independent of the application scenario, the, complementary, pro-active method exploits any a-priori knowledge of the application scenario to adapt more efficiently. A simple example is that as soon as a person with a body sensor node starts walking we know that several aspects, including the network topology, will change. To avoid degradation of network QoS, we pro-actively adapt parameters, in this case, for instance, the frequency of updating the set of neighbouring nodes, as soon as we observe that a person starts to walk. At design time, different modes of operation are selected to be distinguished at run-time. Analysis techniques, such as simulations, are used to determine a suitable configuration for each of these modes. At run time, the approach ensures that nodes can detect the mode in which they should operate. We describe the integration of the pro-active method for two practical monitoring applications. Simulations and experiments show the feasibility of an implementa- tion on resource constrained nodes. The pro-active reconfiguration allows for an efficient QoS provisioning in combination with the re-active approach

A Real-Time Communication Framework for Wireless Sensor Networks

Recent advances in miniaturization and low power design have led to a flurry of activity in wireless sensor networks. Sensor networks have different constraints than traditional wired networks. A wireless sensor network is a special network with large numbers of nodes equipped with embedded processors, sensors, and radios. These nodes collaborate to accomplish a common task such as environment monitoring or asset tracking. In many applications, sensor nodes will be deployed in an ad-hoc fashion without careful planning. They must organize themselves to form a multihop, wireless communication network. In sensor network environments, much research has been conducted in areas such as power consumption, self-organisation techniques, routing between the sensors, and the communication between the sensor and the sink. On the other hand, real-time communication with the Quality of Service (QoS) concept in wireless sensor networks is still an open research field. Most protocols either ignore real time or simply attempt to process as fast as possible and hope that this speed is sufficient to meet the deadline. However, the introduction of real-time communication has created additional challenges in this area. The sensor node spends most of its life routing packets from one node to another until the packet reaches the sink; therefore, the node functions as a small router most of the time. Since sensor networks deal with time-critical applications, it is often necessary for communication to meet real time constraints. However, research that deals with providing QoS guarantees for real-time traffic in sensor networks is still in its infancy.This thesis presents a real-time communication framework to provide quality of service in sensor networks environments. The proposed framework consists of four components: First, present an analytical model for implementing Priority Queuing (PQ) in a sensor node to calculate the queuing delay. The exact packet delay for corresponding classes is calculated. Further, the analytical results are validated through an extensive simulation study. Second, report on a novel analytical model based on a limited service polling discipline. The model is based on an M/D/1 queuing system (a special class of M/G/1 queuing systems), which takes into account two different classes of traffic in a sensor node. The proposed model implements two queues in a sensor node that are served in a round robin fashion. The exact queuing delay in a sensor node for corresponding classes is calculated. Then, the analytical results are validated through an extensive simulation study. Third, exhibit a novel packet delivery mechanism, namely the Multiple Level Stateless Protocol (MLSP), as a real-time protocol for sensor networks to guarantee the traffic in wireless sensor networks. MLSP improves the packet loss rate and the handling of holes in sensor network much better than its counterpart, MMSPEED. It also introduces the k-limited polling model for the first time. In addition, the whole sending packets dropped significantly compared to MMSPEED, which it leads to decrease the consumption power. Fourth, explain a new framework for moving data from the sink to the user, at a low cost and low power, using the Universal Mobile Telecommunication System (UMTS), which is standard for the Third Generation Mobile System (3G). The integration of sensor networks with the 3G mobile network infrastructure will reduce the cost of building new infrastructures and enable the large-scale deployment of sensor network

Cross-layer network lifetime optimization considering transmit and signal processing power in WSNs

Maintaining high energy efficiency is essential for increasing the lifetime of wireless sensor networks (WSNs), where the battery of the sensor nodes cannot be routinely replaced. Nevertheless, the energy budget of the WSN strictly relies on the communication parameters, where the choice of both the transmit power as well as of the modulation and coding schemes (MCSs) plays a significant role in maximizing the network lifetime (NL). In this paper, we optimize the NL of WNSs by analysing the impact of the physical layer parameters as well as of the signal processing power (SPP) P_sp on the NL. We characterize the underlying trade-offs between the NL and bit error ratio (BER) performance for a predetermined set of target signal-to-interference-plus-noise ratio (SINR) values and for different MCSs using periodic transmit-time slot (TS) scheduling in interference-limited WSNs. For a per-link target BER requirement (PLBR) of 10^?3, our results demonstrate that a ’continuous-time’ NL in the range of 0.58?4.99 years is achieved depending on the MCSs, channel configurations, and SPP

A survey of self organisation in future cellular networks

This article surveys the literature over the period of the last decade on the emerging field of self organisation as applied to wireless cellular communication networks. Self organisation has been extensively studied and applied in adhoc networks, wireless sensor networks and autonomic computer networks; however in the context of wireless cellular networks, this is the first attempt to put in perspective the various efforts in form of a tutorial/survey. We provide a comprehensive survey of the existing literature, projects and standards in self organising cellular networks. Additionally, we also aim to present a clear understanding of this active research area, identifying a clear taxonomy and guidelines for design of self organising mechanisms. We compare strength and weakness of existing solutions and highlight the key research areas for further development. This paper serves as a guide and a starting point for anyone willing to delve into research on self organisation in wireless cellular communication networks

Intrusion detection in IPv6-enabled sensor networks.

In this research, we study efficient and lightweight Intrusion Detection Systems (IDS) for ad-hoc networks through the lens of IPv6-enabled Wireless Sensor Actuator Networks. These networks consist of highly constrained devices able to communicate wirelessly in an ad-hoc fashion, thus following the architecture of ad-hoc networks. Current state of the art IDS in IoT and WSNs have been developed considering the architecture of conventional computer networks, and as such they do not efficiently address the paradigm of ad-hoc networks, which is highly relevant in emerging network paradigms, such as the Internet of Things (IoT). In this context, the network properties of resilience and redundancy have not been extensively studied. In this thesis, we first identify a trade-off between the communication and energy overheads of an IDS (as captured by the number of active IDS agents in the network) and the performance of the system in terms of successfully identifying attacks. In order to fine-tune this trade-off, we model networks as Random Geometric Graphs; these are a rigorous approach that allows us to capture underlying structural properties of the network. We then introduce a novel IDS architectural approach that consists of a central IDS agent and set of distributed IDS agents deployed uniformly at random over the network area. These nodes are able to efficiently detect attacks at the networking layer in a collaborative manner by monitoring locally available network information provided by IoT routing protocols, such as RPL. The detailed experimental evaluation conducted in this research demonstrates significant performance gains in terms of communication overhead and energy dissipation while maintaining high detection rates. We also show that the performance of our IDS in ad-hoc networks does not rely on the size of the network but on fundamental underling network properties, such as the network topology and the average degree of the nodes. The experiments show that our proposed IDS architecture is resilient against frequent topology changes due to node failures

Towards persistent structural health monitoring through sustainable wireless sensor networks

This paper documents the design, implementation and characterisation of a wireless sensor node (GENESI Node v1.0), applicable to long-term structural health monitoring. Presented is a three layer abstraction of the hardware platform; consisting of a Sensor Layer, a Main Layer and a Power Layer. Extended operational lifetime is one of the primary design goals, necessitating the inclusion of supplemental energy sources, energy awareness, and the implementation of optimal components (microcontroller(s), RF transceiver, etc.) to achieve lowest-possible power consumption, whilst ensuring that the functional requirements of the intended application area are satisfied. A novel Smart Power Unit has been developed; including intelligence, ambient available energy harvesting (EH), storage, electrochemical fuel cell integration, and recharging capability, which acts as the Power Layer for the node. The functional node has been prototyped, demonstrated and characterised in a variety of operational modes. It is demonstrable via simulation that, under normal operating conditions within a structural health monitoring application, the node may operate perpetually