Joint duty cycle Scheduling, resource Allocation and multi-constrained QoS Routing algorithm

International audienceWireless mesh sensor networks (WMSNs) have recently gained a lot of interest due to their communication capability to support various applications with different Quality of Service (QoS) requirements. The most challenging issue is providing a tradeoff between the resource efficiency and the multiconstrained QoS support. For this purpose, we propose a cross-layer algorithm JSAR (Joint duty cycle Scheduling, resource Allocation and multi-constrained QoS Routing algorithm) for WMSNs on based multi-channel multi-time slot MAC. To our best knowledge, JSAR is the first algorithm which simultaneously combines, a duty cycle scheduling scheme for energy saving, a resource allocation scheme for efficient use of frequency channels and time slots, and an heuristic for multi-constrained routing protocol. The performance of JSAR has been evaluated, showing that it is suitable for on-line implementation

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

Operator Calculus Algorithms for Multi-Constrained Paths

Classical approaches to multi-constrained routing problems generally require construction of trees and the use of heuristics to prevent combinatorial explosion. Introduced here is the notion of constrained path algebras and their application to multi-constrained path problems. The inherent combinatorial properties of these algebras make them useful for routing problems by implicitly pruning the underlying tree structures. Operator calculus (OC) methods are generalized to multiple non-additive constraints in order to develop algorithms for the multi constrained path problem and multi constrained optimization problem. Theoretical underpinnings are developed first, then algorithms are presented. These algorithms demonstrate the tremendous simplicity, flexibility and speed of the OC approach. Algorithms are implemented in Mathematica and Java and applied to a problem first proposed by Ben Slimane et al. as an example

A critical analysis of research potential, challenges and future directives in industrial wireless sensor networks

In recent years, Industrial Wireless Sensor Networks (IWSNs) have emerged as an important research theme with applications spanning a wide range of industries including automation, monitoring, process control, feedback systems and automotive. Wide scope of IWSNs applications ranging from small production units, large oil and gas industries to nuclear fission control, enables a fast-paced research in this field. Though IWSNs offer advantages of low cost, flexibility, scalability, self-healing, easy deployment and reformation, yet they pose certain limitations on available potential and introduce challenges on multiple fronts due to their susceptibility to highly complex and uncertain industrial environments. In this paper a detailed discussion on design objectives, challenges and solutions, for IWSNs, are presented. A careful evaluation of industrial systems, deadlines and possible hazards in industrial atmosphere are discussed. The paper also presents a thorough review of the existing standards and industrial protocols and gives a critical evaluation of potential of these standards and protocols along with a detailed discussion on available hardware platforms, specific industrial energy harvesting techniques and their capabilities. The paper lists main service providers for IWSNs solutions and gives insight of future trends and research gaps in the field of IWSNs

Dynamic Network State Learning Model for Mobility Based WMSN Routing Protocol

The rising demand of wireless multimedia sensor networks (WMSNs) has motivated academia-industries to develop energy efficient, Quality of Service (QoS) and delay sensitive communication systems to meet major real-world demands like multimedia broadcast, security and surveillance systems, intelligent transport system, etc. Typically, energy efficiency, QoS and delay sensitive transmission are the inevitable requirements of WMSNs. Majority of the existing approaches either use physical layer or system level schemes that individually can’t assure optimal transmission decision to meet the demand. The cumulative efficiency of physical layer power control, adaptive modulation and coding and system level dynamic power management (DPM) are found significant to achieve these demands. With this motivation, in this paper a unified model is derived using enhanced reinforcement learning and stochastic optimization method. Exploiting physical as well as system level network state information, our proposed dynamic network state learning model (NSLM) applies stochastic optimization to learn network state-activity that derives an optimal DPM policy and PHY switching scheduling. NSLM applies known as well as unknown network state variables to derive transmission and PHY switching policy, where it considers DPM as constrained Markov decision process (MDP) problem. Here,the use of Hidden Markov Model and Lagrangian relaxation has made NSLM convergence swift that assures delay-sensitive, QoS enriched, and bandwidth and energy efficient transmission for WMSN under uncertain network conditions. Our proposed NSLM DPM model has outperformed traditional Q-Learning based DPM in terms of buffer cost, holding cost, overflow, energy consumption and bandwidth utilization

Review of Optimization Problems in Wireless Sensor Networks

International audienc