Deployment Policies to Reliably Maintain and Maximize Expected Coverage in a Wireless Sensor Network

The long-term operation of a wireless sensor network (WSN) requires the deployment of new sensors over time to restore any loss in network coverage and communication ability resulting from sensor failures. Over the course of several deployment actions it is important to consider the cost of maintaining the WSN in addition to any desired performance measures such as coverage, connectivity, or reliability. The resulting problem formulation is approached first through a time-based deployment model in which the network is restored to a fixed size at periodic time intervals. The network destruction spectrum (D-spectrum) has been introduced to estimate reliability and is more commonly applied to a static network, rather than a dynamic network where new sensors are deployed over time. We discuss how the D-spectrum can be incorporated to estimate reliability of a time-based deployment policy and the features that allow a wide range of deployment policies to be evaluated in an efficient manner. We next focus on a myopic condition-based deployment model where the network is observed at periodic time intervals and a fixed budget is available to deploy new sensors with each observation. With a limited budget available the model must address the complexity present in a dynamic network size in addition to a dynamic network topology, and the dependence of network reliability on the deployment action. We discuss how the D-spectrum can be applied to the myopic condition-based deployment problem, illustrating the value of the D-spectrum in a variety of maintenance settings beyond the traditional static network reliability problem. From the insight of the time-based and myopic condition-based deployment models, we present a Markov decision process (MDP) model for the condition-based deployment problem that captures the benefit of an action beyond the current time period. Methodology related to approximate dynamic programming (ADP) and approximate value iteration algorithms is presented to search for high quality deployment policies. In addition to the time-based and myopic condition-based deployment models, the MDP model is one of the few addressing the repeated deployment of new sensors as well as an emphasis on network reliability. For each model we discuss the relevant problem formulation, methodology to estimate network reliability, and demonstrate the performance in a range of test instances, comparing to alternative policies or models as appropriate. We conclude with a stochastic optimization model focused on a slightly different objective to maximize expected coverage with uncertainty in where a sensor lands in the network. We discuss a heuristic solution method that seeks to determine an optimal deployment of sensors, present results for a wide range of network sizes and explore the impact of sensor failures on both the model formulation and resulting deployment policy

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

An Adaptive Multimedia-Oriented Handoff Scheme for IEEE 802.11 WLANs

Previous studies have shown that the actual handoff schemes employed in the IEEE 802.11 Wireless LANs (WLANs) do not meet the strict delay constraints placed by many multimedia applications like Voice over IP. Both the active and the passive supported scan modes in the standard handoff procedure have important delay that affects the Quality of Service (QoS) required by the real-time communications over 802.11 networks. In addition, the problem is further compounded by the fact that limited coverage areas of Access Points (APs) occupied in 802.11 infrastructure WLANs create frequent handoffs. We propose a new optimized and fast handoff scheme that decrease both handoff latency and occurrence by performing a seamless prevent scan process and an effective next-AP selection. Through simulations and performance evaluation, we show the effectiveness of the new adaptive handoff that reduces the process latency and adds new context-based parameters. The Results illustrate a QoS delay-respect required by applications and an optimized AP-choice that eliminates handoff events that are not beneficial.Comment: 20 pages, 14 figures, 4 table

PERFORMANCE ANALYSIS AND OPTIMIZATION OF QUERY-BASED WIRELESS SENSOR NETWORKS

This dissertation is concerned with the modeling, analysis, and optimization of large-scale, query-based wireless sensor networks (WSNs). It addresses issues related to the time sensitivity of information retrieval and dissemination, network lifetime maximization, and optimal clustering of sensor nodes in mobile WSNs. First, a queueing-theoretic framework is proposed to evaluate the performance of such networks whose nodes detect and advertise significant events that are useful for only a limited time; queries generated by sensor nodes are also time-limited. The main performance parameter is the steady state proportion of generated queries that fail to be answered on time. A scalable approximation for this parameter is first derived assuming the transmission range of sensors is unlimited. Subsequently, the proportion of failed queries is approximated using a finite transmission range. The latter approximation is remarkably accurate, even when key model assumptions related to event and query lifetime distributions and network topology are violated. Second, optimization models are proposed to maximize the lifetime of a query-based WSN by selecting the transmission range for all of the sensor nodes, the resource replication level (or time-to-live counter) and the active/sleep schedule of nodes, subject to connectivity and quality-of-service constraints. An improved lower bound is provided for the minimum transmission range needed to ensure no network nodes are isolated with high probability. The optimization models select the optimal operating parameters in each period of a finite planning horizon, and computational results indicate that the maximum lifetime can be significantly extended by adjusting the key operating parameters as sensors fail over time due to energy depletion. Finally, optimization models are proposed to maximize the demand coverage and minimize the costs of locating, and relocating, cluster heads in mobile WSNs. In these models, the locations of mobile sensor nodes evolve randomly so that each sensor must be optimally assigned to a cluster head during each period of a finite planning horizon. Additionally, these models prescribe the optimal times at which to update the sensor locations to improve coverage. Computational experiments illustrate the usefulness of dynamically updating cluster head locations and sensor location information over time