Resource-aware task scheduling by an adversarial bandit solver method in wireless sensor networks

This article was published in the Eurasip Journal on Wireless Communications and Networking [©2016 Springer International Publishing.] and the definite version is available at: http://dx.doi.org/10.1186/s13638-015-0515-y. The article website is at: http://jwcn.eurasipjournals.springeropen.com/articles/10.1186/s13638-015-0515-yA wireless sensor network (WSN) is composed of a large number of tiny sensor nodes. Sensor nodes are very resource-constrained, since nodes are often battery-operated and energy is a scarce resource. In this paper, a resource-aware task scheduling (RATS) method is proposed with better performance/resource consumption trade-off in a WSN. Particularly, RATS exploits an adversarial bandit solver method called exponential weight for exploration and exploitation (Exp3) for target tracking application of WSN. The proposed RATS method is compared and evaluated with the existing scheduling methods exploiting online learning: distributed independent reinforcement learning (DIRL), reinforcement learning (RL), and cooperative reinforcement learning (CRL), in terms of the tracking quality/energy consumption trade-off in a target tracking application. The communication overhead and computational effort of these methods are also computed. Simulation results show that the proposed RATS outperforms the existing methods DIRL and RL in terms of achieved tracking performance. © 2016, Khan.Publishe

Resource Allocation in Networking and Computing Systems: A Security and Dependability Perspective

In recent years, there has been a trend to integrate networking and computing systems, whose management is getting increasingly complex. Resource allocation is one of the crucial aspects of managing such systems and is affected by this increased complexity. Resource allocation strategies aim to effectively maximize performance, system utilization, and profit by considering virtualization technologies, heterogeneous resources, context awareness, and other features. In such complex scenario, security and dependability are vital concerns that need to be considered in future computing and networking systems in order to provide the future advanced services, such as mission-critical applications. This paper provides a comprehensive survey of existing literature that considers security and dependability for resource allocation in computing and networking systems. The current research works are categorized by considering the allocated type of resources for different technologies, scenarios, issues, attributes, and solutions. The paper presents the research works on resource allocation that includes security and dependability, both singularly and jointly. The future research directions on resource allocation are also discussed. The paper shows how there are only a few works that, even singularly, consider security and dependability in resource allocation in the future computing and networking systems and highlights the importance of jointly considering security and dependability and the need for intelligent, adaptive and robust solutions. This paper aims to help the researchers effectively consider security and dependability in future networking and computing systems.publishedVersio

Efficient consensus algorithm for the accurate faulty node tracking with faster convergence rate in a distributed sensor network

This article was published in the Eurasip Journal on Wireless Communications and Networking [©2016 Published by Springer International Publishing.] and the definite version is available at: http://dx.doi.org/10.1186/s13638-016-0698-x . The article website is at:http://jwcn.eurasipjournals.springeropen.com/articles/10.1186/s13638-016-0698-xOne of the challenging issues in a distributed computing system is to reach on a decision with the presence of so many faulty nodes. These faulty nodes may update the wrong information, provide misleading results and may be nodes with the depleted battery power. Consensus algorithms help to reach on a decision even with the faulty nodes. Every correct node decides some values by a consensus algorithm. If all correct nodes propose the same value, then all the nodes decide on that. Every correct node must agree on the same value. Faulty nodes do not reach on the decision that correct nodes agreed on. Binary consensus algorithm and average consensus algorithm are the most widely used consensus algorithm in a distributed system. We apply binary consensus and average consensus algorithm in a distributed sensor network with the presence of some faulty nodes. We evaluate these algorithms for better convergence rate and error rate. © 2016, The Author(s).Publishe

Resource Aware Task Scheduling in Wireless Sensor Networks

Drahtlose Sensornetzwerke ( WSN ) sind eine attraktive Plattform fÃr verschiedene Pervasive Computing -Anwendungen. Eine typische Anwendung ist der WSN diff erent Aufgaben, die auf jeder Sensorknoten geplant werden mÃssen, zusammengesetzt ist. Jedoch stellen die schweren RessourcenbeschrÃ$nkungen eine besondere Herausforderung fÃr die Entwicklung von WSN -Anwendungen, und die Planung von Aufgaben hat in der Regel einen starken Einfluss auf die erzielbare Leistung und Energieverbrauch . In dieser Arbeit schlagen wir diff erent Methoden zur Planung der Aufgaben, bei denen jeder Knoten bestimmt die nÃ$chste Aufgabe auf der Basis der beobachteten Verhalten der Anwendung . Wir schlagen vor, einen Rahmen , wo wir die Anwendungsleistung und die erforderliche Energieverbrauch durch eine gewichtete Belohnungsfunktion handeln kann und daher erreichen di fferent Energie / Leistungsergebnisse der Gesamtapplikation . Durch den Austausch von Daten zwischen den Nachbarknotenwir weiter verbessern kann Diese Energie / Leistungshandel-off . Wir bewerten unsere AnsÃ$tze in einem Ziel Tracking-Anwendung . Unsere Simulationen zeigen, dass kooperative AnsÃ$tze Ãberlegen sind nicht- kooperative AnsÃ$tze fÃr diese Art von Anwendungen.Wireless sensor networks (WSN) are an attractive platform for various pervasive computing applications. A typical WSN application is composed of different tasks which need to be scheduled on each sensor node. However, the severe resource limitations pose a particular challenge for developing WSN applications, and the scheduling of tasks has typically a strong influence on the achievable performance and energy consumption. In this thesis we propose different methods for scheduling the tasks where each node determines the next task based on the observed application behavior. We propose a framework where we can trade the application performance and the required energy consumption by a weighted reward function and can therefore achieve different energy/performance results of the overall application. By exchanging data among neighboring nodes we can further improve this energy/performance trade-off. We evaluate our approaches in a target tracking application. Our simulations show that cooperative approaches are superior to non-cooperative approaches for this kind of applications.Muhidul Islam KhanAbweichender Titel laut Übersetzung der Verfasserin/des VerfassersKlagenfurt, Alpen-Adria-Univ., Diss., 2014OeBB(VLID)241002

Q-Learning Based Joint Energy-Spectral Efficiency Optimization in Multi-Hop Device-to-Device Communication

In scenarios, like critical public safety communication networks, On-Scene Available (OSA) user equipment (UE) may be only partially connected with the network infrastructure, e.g., due to physical damages or on-purpose deactivation by the authorities. In this work, we consider multi-hop Device-to-Device (D2D) communication in a hybrid infrastructure where OSA UEs connect to each other in a seamless manner in order to disseminate critical information to a deployed command center. The challenge that we address is to simultaneously keep the OSA UEs alive as long as possible and send the critical information to a final destination (e.g., a command center) as rapidly as possible, while considering the heterogeneous characteristics of the OSA UEs. We propose a dynamic adaptation approach based on machine learning to improve a joint energy-spectral efficiency (ESE). We apply a Q-learning scheme in a hybrid fashion (partially distributed and centralized) in learner agents (distributed OSA UEs) and scheduler agents (remote radio heads or RRHs), for which the next hop selection and RRH selection algorithms are proposed. Our simulation results show that the proposed dynamic adaptation approach outperforms the baseline system by approximately 67% in terms of joint energy-spectral efficiency, wherein the energy efficiency of the OSA UEs benefit from a gain of approximately 30%. Finally, the results show also that our proposed framework with C-RAN reduces latency by approximately 50% w.r.t. the baseline

Performance Analysis of Resource-Aware Task Scheduling Methods in Wireless Sensor Networks

Wireless sensor networks (WSNs) are an attractive platform for monitoring and measuring physical phenomena. WSNs typically consist of hundreds or thousands of battery-operated tiny sensor nodes which are connected via a low data rate wireless network. A WSN application, such as object tracking or environmental monitoring, is composed of individual tasks which must be scheduled on each node. Naturally the order of task execution influences the performance of the WSN application. Scheduling the tasks such that the performance is increased while the energy consumption remains low is a key challenge. In this paper we apply online learning to task scheduling in order to explore the tradeoff between performance and energy consumption. This helps to dynamically identify effective scheduling policies for the sensor nodes. The energy consumption for computation and communication is represented by a parameter for each application task. We compare resource-aware task scheduling based on three online learning methods: independent reinforcement learning (RL), cooperative reinforcement learning (CRL), and exponential weight for exploration and exploitation (Exp3). Our evaluation is based on the performance and energy consumption of a prototypical target tracking application. We further determine the communication overhead and computational effort of these methods

Cooperative game theory based load balancing in long term evolution network

This conference paper was presented in the 1st International Conference on Computer and Information Engineering, ICCIE 2015; Department of Computer Science and Engineering (CSE) of Rajshahi University of Engineering and Technology (RUET)Rajshahi; Bangladesh; 26 November 2015 through 27 November 2015 [© 2015 Institute of Electrical and Electronics Engineers Inc.] The conference paper's definite version is available at: http://10.1109/CCIE.2015.7399302Long term evolution (LTE) network, incompatible with 2G and 3G networks is the most promising technology for wireless communication with higher speed and capacity. Self-organized load balancing is an important research issue for the wireless networks. Game theory provides an efficient way to provide self-organizing properties in a distributed environment like LTE networks. Load balancing means to assign users from highly loaded cells to neighbor lower loaded cells. The amount of load needs to be offloaded or accepted by a particular cell is not really specified and currently totally vendor specified. In our proposed cooperative game theoretic approach, each cell is considered as a player where they trade the load by forming a coalition by satisfying the overall performance of the network. Simulation results show that our proposed method provides better performance in terms of satisfied users and adjusted load values

Resource coordination in Wireless Sensor Networks by combinatorial auction based method

Wireless Sensor Networks (WSN) consists of small sensor devices with sensing, processing and communication capabilities. Sensor nodes are operated by batteries. As the replacement of these batteries are not practical, this network is very much energy sensitive. Resource coordination is an important issue to make this system energy efficient. Sensor nodes can be applied in various applications. Object tracking, routing, event detection are some common applications in WSN. These application needs to perform some tasks like sensing, transmitting, sleeping, receiving etc. At each time step, the sensor nodes need to perform one task based on its application demand. Scheduling of these tasks is very important aspect for WSN in order to coordinate the resources. In this paper, an effective market based method is proposed for resource coordination in WSN. At first the description of the problem is presented then the combinatorial auction based method is proposed. The simulation results show the efficiency of the proposed method comparing with other existing methods. \ua9 2012 IEEE

Paper Title (use style: paper title)

Abstract-Wireless Sensor Networks (WSN) consists of small sensor devices with sensing, processing and communication capabilities. Sensor nodes are operated by batteries. As the replacement of these batteries are not practical, this network is very much energy sensitive. Resource coordination is an important issue to make this system energy efficient. Sensor nodes can be applied in various applications. Object tracking, routing, event detection are some common applications in WSN. These application needs to perform some tasks like sensing, transmitting, sleeping, receiving etc. At each time step, the sensor nodes need to perform one task based on its application demand. Scheduling of these tasks is very important aspect for WSN in order to coordinate the resources. In this paper, an effective market based method is proposed for resource coordination in WSN. At first the description of the problem is presented then the combinatorial auction based method is proposed. The simulation results show the efficiency of the proposed method comparing with other existing methods