An Energy Driven Architecture for Wireless Sensor Networks

Most wireless sensor networks operate with very limited energy sources-their batteries, and hence their usefulness in real life applications is severely constrained. The challenging issues are how to optimize the use of their energy or to harvest their own energy in order to lengthen their lives for wider classes of application. Tackling these important issues requires a robust architecture that takes into account the energy consumption level of functional constituents and their interdependency. Without such architecture, it would be difficult to formulate and optimize the overall energy consumption of a wireless sensor network. Unlike most current researches that focus on a single energy constituent of WSNs independent from and regardless of other constituents, this paper presents an Energy Driven Architecture (EDA) as a new architecture and indicates a novel approach for minimising the total energy consumption of a WS

A Survey on Delay-Aware Resource Control for Wireless Systems --- Large Deviation Theory, Stochastic Lyapunov Drift and Distributed Stochastic Learning

In this tutorial paper, a comprehensive survey is given on several major systematic approaches in dealing with delay-aware control problems, namely the equivalent rate constraint approach, the Lyapunov stability drift approach and the approximate Markov Decision Process (MDP) approach using stochastic learning. These approaches essentially embrace most of the existing literature regarding delay-aware resource control in wireless systems. They have their relative pros and cons in terms of performance, complexity and implementation issues. For each of the approaches, the problem setup, the general solution and the design methodology are discussed. Applications of these approaches to delay-aware resource allocation are illustrated with examples in single-hop wireless networks. Furthermore, recent results regarding delay-aware multi-hop routing designs in general multi-hop networks are elaborated. Finally, the delay performance of the various approaches are compared through simulations using an example of the uplink OFDMA systems.Comment: 58 pages, 8 figures; IEEE Transactions on Information Theory, 201

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

A cross layer multi hop network architecture for wireless Ad Hoc networks

In this paper, a novel decentralized cross-layer multi-hop cooperative network architecture is presented. Our architecture involves the design of a simple yet efficient cooperative flooding scheme,two decentralized opportunistic cooperative forwarding mechanisms as well as the design of Routing Enabled Cooperative Medium Access Control (RECOMAC) protocol that spans and incorporates the physical, medium access control (MAC) and routing layers for improving the performance of multihop communication. The proposed architecture exploits randomized coding at the physical layer to realize cooperative diversity. Randomized coding alleviates relay selection and actuation mechanisms,and therefore reduces the coordination among the relays. The coded packets are forwarded via opportunistically formed cooperative sets within a region, without communication among the relays and without establishing a prior route. In our architecture, routing layer functionality is submerged into the MAC layer to provide seamless cooperative communication while the messaging overhead to set up routes, select and actuate relays is minimized. RECOMAC is shown to provide dramatic performance improvements, such as eight times higher throughput and ten times lower end-to-end delay as well as reduced overhead, as compared to networks based on well-known IEEE 802.11 and Ad hoc On Demand Distance Vector (AODV) protocols