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

Dataflow Analysis for Real-Time Embedded Multiprocessor System Design

Dataflow analysis techniques are key to reduce the number of design iterations and shorten the design time of real-time embedded network based multiprocessor systems that process data streams. With these analysis techniques the worst-case end-to-end temporal behavior of hard real-time applications can be derived from a dataflow model in which computation, communication and arbitration is modeled. For soft real-time applications these static dataflow analysis techniques are combined with simulation of the dataflow model to test statistical assertions about their temporal behavior. The simulation results in combination with properties of the dataflow model are used to derive the sensitivity of design parameters and to estimate parameters like the capacity of data buffers

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