Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

Submicron Systems Architecture: Semiannual Technical Report

No abstract available

Information-Centric Multilayer Networking: Improving Performance Through an ICN/WDM Architecture

Information-centric networking (ICN) facilitates content identification in networks and offers parametric representation of content semantics. This paper proposes an ICN/WDM network architecture that uses these features to offer superior network utilization, in terms of performance and power consumption. The architecture introduces an ICN publish/subscribe communication approach to the wavelength layer, whereby content is aggregated according to its popularity rank into wavelength-size groups that can be published and subscribed to by multiple nodes. Consequently, routing and wavelength assignment (RWA) algorithms benefit from anycast to identify multiple sources of aggregate content and allow optimization of the source selection of light paths. A power-aware algorithm, maximum degree of connectivity, has been developed with the objective of exploiting this flexibility to address the tradeoff between power consumption and network performance. The algorithm is also applicable to IP architectures, albeit with less flexibility. Evaluation results indicate the superiority of the proposed ICN architecture, even when utilizing conventional routing methods, compared with its IP counterpart. The results further highlight the performance improvement achieved by the proposed algorithm, compared with the conventional RWA methods, such as shortest-path first fit

Human-Machine Collaborative Optimization via Apprenticeship Scheduling

Coordinating agents to complete a set of tasks with intercoupled temporal and resource constraints is computationally challenging, yet human domain experts can solve these difficult scheduling problems using paradigms learned through years of apprenticeship. A process for manually codifying this domain knowledge within a computational framework is necessary to scale beyond the ``single-expert, single-trainee" apprenticeship model. However, human domain experts often have difficulty describing their decision-making processes, causing the codification of this knowledge to become laborious. We propose a new approach for capturing domain-expert heuristics through a pairwise ranking formulation. Our approach is model-free and does not require enumerating or iterating through a large state space. We empirically demonstrate that this approach accurately learns multifaceted heuristics on a synthetic data set incorporating job-shop scheduling and vehicle routing problems, as well as on two real-world data sets consisting of demonstrations of experts solving a weapon-to-target assignment problem and a hospital resource allocation problem. We also demonstrate that policies learned from human scheduling demonstration via apprenticeship learning can substantially improve the efficiency of a branch-and-bound search for an optimal schedule. We employ this human-machine collaborative optimization technique on a variant of the weapon-to-target assignment problem. We demonstrate that this technique generates solutions substantially superior to those produced by human domain experts at a rate up to 9.5 times faster than an optimization approach and can be applied to optimally solve problems twice as complex as those solved by a human demonstrator.Comment: Portions of this paper were published in the Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI) in 2016 and in the Proceedings of Robotics: Science and Systems (RSS) in 2016. The paper consists of 50 pages with 11 figures and 4 table