An elementary proposition on the dynamic routing problem in wireless networks of sensors

The routing problem (finding an optimal route from one point in a computer network to another) is surrounded by impossibility results. These results are usually expressed as lower and upper bounds on the set of nodes (or the set of links) of a network and represent the complexity of a solution to the routing problem (a routing function). The routing problem dealt with here, in particular, is a dynamic one (it accounts for network dynamics) and concerns wireless networks of sensors. Sensors form wireless links of limited capacity and time-variable quality to route messages amongst themselves. It is desired that sensors self-organize ad hoc in order to successfully carry out a routing task, e.g. provide daily soil erosion reports for a monitored watershed, or provide immediate indications of an imminent volcanic eruption, in spite of network dynamics. Link dynamics are the first barrier to finding an optimal route between a node x and a node y in a sensor network. The uncertainty of the outcome (the best next hop) of a routing function lies partially with the quality fluctuations of wireless links. Take, for example, a static network. It is known that, given the set of nodes and their link weights (or costs), a node can compute optimal routes by running, say, Dijkstra's algorithm. Link dynamics however suggest that costs are not static. Hence, sensors need a metric (a measurable quantity of uncertainty) to monitor for fluctuations, either improvements or degradations of quality or load; when a fluctuation is sufficiently large (say, by Delta), sensors ought to update their costs and seek another route. Therein lies the other fundamental barrier to find an optimal route - complexity. A crude argument would suggest that sensors (and their links) have an upper bound on the number of messages they can transmit, receive and store due to resource constraints. Such messages can be application traffic, in which case it is desirable, or control traffic, in which case it should be kept minimal. The first type of traffic is demand, and a user should provision for it accordingly. The second type of traffic is overhead, and it is necessary if a routing system (or scheme) is to ensure its fidelity to the application requirements (policy). It is possible for a routing scheme to approximate optimal routes (by Delta) by reducing its message and/or memory complexity. The common denominator of the routing problem and the desire to minimize overhead while approximating optimal routes is Delta, the deviation (or stretch) of a computed route from an optimal one, as computed by a node that has instantaneous knowledge of the set of all nodes and their interaction costs (an oracle). This dissertation deals with both problems in unison. To do so, it needs to translate the policy space (the user objectives) into a metric space (routing objectives). It does so by means of a cost function that normalizes metrics into a number of hops. Then it proceeds to devise, design, and implement a scheme that computes minimum-hop-count routes with manageable complexity. The theory presented is founded on (well-ordered) sets with respect to an elementary proposition, that a route from a source x to a destination y can be computed either by y sending an advertisement to the set of all nodes, or by x sending a query to the set of all nodes; henceforth the proactive method (of y) and the reactive method (of x), respectively. The debate between proactive and reactive routing protocols appears in many instances of the routing problem (e.g. routing in mobile networks, routing in delay-tolerant networks, compact routing), and it is focussed on whether nodes should know a priori all routes and then select the best one (with the proactive method), or each node could simply search for a (hopefully best) route on demand (with the reactive method). The proactive method is stateful, as it requires the entire metric space - the set of nodes and their interaction costs - in memory (in a routing table). The routes computed by the proactive method are optimal and the lower and upper bounds of proactive schemes match those of an oracle. Any attempt to reduce the proactive overhead, e.g. by introducing hierarchies, will result in sub-optimal routes (of known stretch). The reactive method is stateless, as it requires no information whatsoever to compute a route. Reactive schemes - at least as they are presently understood - compute sub-optimal routes (and thus far, of unknown stretch). This dissertation attempts to answer the following question: "what is the least amount of state required to compute an optimal route from a source to a destination?" A hybrid routing scheme is used to investigate this question, one that uses the proactive method to compute routes to near destinations and the reactive method for distant destinations. It is shown that there are cases where hybrid schemes can converge to optimal routes, despite possessing incomplete routing state, and that the necessary and sufficient condition to compute optimal routes with local state alone is related neither to the size nor the density of a network; it is rather the circumference (the size of the largest cycle) of a network that matters. Counterexamples, where local state is insufficient, are discussed to derive the worst-case stretch. The theory is augmented with simulation results and a small experimental testbed to motivate the discussion on how policy space (user requirements) can translate into metric spaces and how different metrics affect performance. On the debate between proactive and reactive protocols, it is shown that the two classes are equivalent. The dissertation concludes with a discussion on the applicability of its results and poses some open problems

Rapid Learning Optimization Approach for Battery Recovery-Aware Embedded System Communications

To date, battery optimization for embedded systems still a crucial subject. Actually, the majority of carried out works focus on transmission controls without taking into account the specifications of the batteries themselves. Indeed, an improvementof 70\% is reported by exploiting the battery recovery effect.In this paper, the recovery phenomenon is exploited to design an algorithm that optimizes both the lifetime of the battery and the performance of the studied system. The algorithms from Dynamic programming and Reinforcement learning fields are the first to be considered. When in Dynamic programming prior detailed information are assumed to be available, in reinforcement learning those information becomes unknown and long calculation times are needed to converge toward an optimal policy solution. The paper contribution is about designing a new Rapid Learning Algorithm (RLA) that combines both Dynamic programming and Reinforcement learning features. RLA exploits a reduced model of the system instead of exploring the whole and heavy system state model as Dynamic programming do. The RLA run-time is then shortened. Based on battery stochastic model, the simulation results obtained with RLA are compared to the Dynamic programming and Reinforcement learning algorithms under the same conditions. By taking into account the recovery effect this paper illustrates that the calculation time and the system performance are greatly improved when RLA is adopted

Node Deployment in Heterogeneous Rayleigh Fading Sensor Networks

We study a heterogeneous Rayleigh fading wireless sensor network (WSN) in which densely deployed sensor nodes monitor an environment and transmit their sensed information to base stations (BSs) using access points (APs) as relays to facilitate the data transfer. We consider both large-scale and small-scale propagation effects in our system model and formulate the node deployment problem as an optimization problem aimed at minimizing the wireless communication network's power consumption. By imposing a desired outage probability constraint on all communication channels, we derive the necessary conditions for the optimal deployment that not only minimize the power consumption, but also guarantee all wireless links to have an outage probability below the given threshold. In addition, we study the necessary conditions for an optimal deployment given ergodic capacity constraints. We compare our node deployment algorithms with similar algorithms in the literature and demonstrate their efficacy and superiority

Unified Role Assignment Framework For Wireless Sensor Networks

Wireless sensor networks are made possible by the continuing improvements in embedded sensor, VLSI, and wireless radio technologies. Currently, one of the important challenges in sensor networks is the design of a systematic network management framework that allows localized and collaborative resource control uniformly across all application services such as sensing, monitoring, tracking, data aggregation, and routing. The research in wireless sensor networks is currently oriented toward a cross-layer network abstraction that supports appropriate fine or course grained resource controls for energy efficiency. In that regard, we have designed a unified role-based service paradigm for wireless sensor networks. We pursue this by first developing a Role-based Hierarchical Self-Organization (RBSHO) protocol that organizes a connected dominating set (CDS) of nodes called dominators. This is done by hierarchically selecting nodes that possess cumulatively high energy, connectivity, and sensing capabilities in their local neighborhood. The RBHSO protocol then assigns specific tasks such as sensing, coordination, and routing to appropriate dominators that end up playing a certain role in the network. Roles, though abstract and implicit, expose role-specific resource controls by way of role assignment and scheduling. Based on this concept, we have designed a Unified Role-Assignment Framework (URAF) to model application services as roles played by local in-network sensor nodes with sensor capabilities used as rules for role identification. The URAF abstracts domain specific role attributes by three models: the role energy model, the role execution time model, and the role service utility model. The framework then generalizes resource management for services by providing abstractions for controlling the composition of a service in terms of roles, its assignment, reassignment, and scheduling. To the best of our knowledge, a generic role-based framework that provides a simple and unified network management solution for wireless sensor networks has not been proposed previously

Optimisation of Mobile Communication Networks - OMCO NET

The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University. The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing

Robust Controller for Delays and Packet Dropout Avoidance in Solar-Power Wireless Network

Solar Wireless Networked Control Systems (SWNCS) are a style of distributed control systems where sensors, actuators, and controllers are interconnected via a wireless communication network. This system setup has the benefit of low cost, flexibility, low weight, no wiring and simplicity of system diagnoses and maintenance. However, it also unavoidably calls some wireless network time delays and packet dropout into the design procedure. Solar lighting system offers a clean environment, therefore able to continue for a long period. SWNCS also offers multi Service infrastructure solution for both developed and undeveloped countries. The system provides wireless controller lighting, wireless communications network (WI-FI/WIMAX), CCTV surveillance, and wireless sensor for weather measurement which are all powered by solar energy

Uncertainty Management of Intelligent Feature Selection in Wireless Sensor Networks

Wireless sensor networks (WSN) are envisioned to revolutionize the paradigm of monitoring complex real-world systems at a very high resolution. However, the deployment of a large number of unattended sensor nodes in hostile environments, frequent changes of environment dynamics, and severe resource constraints pose uncertainties and limit the potential use of WSN in complex real-world applications. Although uncertainty management in Artificial Intelligence (AI) is well developed and well investigated, its implications in wireless sensor environments are inadequately addressed. This dissertation addresses uncertainty management issues of spatio-temporal patterns generated from sensor data. It provides a framework for characterizing spatio-temporal pattern in WSN. Using rough set theory and temporal reasoning a novel formalism has been developed to characterize and quantify the uncertainties in predicting spatio-temporal patterns from sensor data. This research also uncovers the trade-off among the uncertainty measures, which can be used to develop a multi-objective optimization model for real-time decision making in sensor data aggregation and samplin