Mobile Ad-Hoc Network (MANET) facilitates the creation of dynamic reconfigurable networks, without centralized infrastructure. MANET routing protocols have to face high challenges like link instability, node mobility, frequently changing topologies and energy consumption of node, due to these challenges routing becomes one of the core issues in MANETs. This Thesis mainly focuses on the reactive routing protocol such as Ad-Hoc On-Demand Distance Vector (AODV) routing protocol. Reliable and Efficient Reactive Routing Protocol (RERRP) for MANET has been proposed to reduce the link breakages between the moving nodes. This scheme selects a reliable route using Reliability Factor (RF); the RF considers Route Expiration Time and Hop Count to select a routing path with high reliability and have less number of hops. The simulation result shows that RERRP outperforms AODV and enhance the packet delivery fraction (PDF) by around 6% and reduces the network routing load (NRL) by around 30%. Broadcasting in MANET could cause serious redundancy, contention, and collision of the packets. A scheme, Effective Broadcast Control Routing Protocol (EBCRP) has been proposed for the controlling of broadcast storm problem in a MANET. The EBCRP is mainly selects the reliable node while controlling the redundant re-broadcast of the route request packet. The proposed algorithm EBCRP is an on-demand routing protocol, therefore AODV route discovery mechanism was selected as the base of this scheme. The analysis of the performance of EBCRP has revealed that the EBCRP have controlled the routing overhead significantly, reduces it around 70% and enhance the packet delivery by 13% as compared to AODV. An Energy Sensible and Route Stability Based Routing Protocol (ESRSBR) have also been proposed that mainly focuses on increasing the network lifetime with better packet delivery. The ESRSBR supports those nodes to participate in the data transfer that have more residual energy related to their neighbour nodes. The proposed protocol also keeps track of the stability of the links between the nodes. Finally, the ESRSBR selects those routes which consist of nodes that have more residual energy and have stable links. The comparative analysis of ESRSBR with AODV and recently proposed routing protocol called Link Stability and Energy Aware (LSEA) routing protocol revealed that the proposed protocol ESRSBR has a significantly affect the network lifetime, increases it around 10% and 13% as compared to LSEA and AODV protocols respectively. The ESRSBR also decreases the routing overhead by 22% over LSEA and by 38% over AODV.

"Due to the dramatic growth in the use of Wireless Sensor Network (WSN) applications - ranging from environment and habitat monitoring to tracking and surveillance, network research in WSN protocols has been very active in the last decade. With battery-powered sensors operating in unattended environments, energy conservation becomes the key technique for improving WSN lifetimes. WSN Medium Access Control (MAC) protocols address energy awareness and reduced duty cycles since the radio is the component that consumes most of the energy. This thesis investigates the performance of two recently published energy-aware MAC protocols, Crankshaft and SCP-MAC. Crankshaft has been shown to be one of the best protocols in terms of energy consumption in dense WSNs while SCP-MAC has a dedicated low duty cycle and low average latencies. The focus of this investigation is to discover techniques for reducing the latency of Crankshaft. Using OMNeT++, an open source and component-based simulation framework, this study investigates possible modifications to Crankshaft to improve its latency. The potential improvements considered include modifications to Crankshaft’s retransmission contention scheme (Sift), adjustments to its inherent settings, and investigating the impact of ACKs. Since OMNeT++ readily provided only a variant of SCP-MAC identified as SCP-MAC*, the simulations results presented involve comparing variants of both protocols (Crankshaft and SCP-MAC*). The performance of these protocols is also analyzed using distinct sensor node communication patterns. It was determined that Crankshaft’s latency depends on its ACK/Retransmission settings. Specifically, Crankshaft has the best latency with No ACKs, without much loss in energy consumption. But the latency can also be improved when ACKs are enabled by reducing the number of retries. Furthermore, the latency and delivery ratio are also directly governed by the WSN traffic pattern and the congestion in the network, as there was a noticeable improvement for both parameters in one-hop traffic, compared to multi-hop convergecast traffic to the sink. Finally, it was observed that Crankshaft’s broadcast performance in flooding traffic can be improved by increasing the number of broadcast slots used, though this is detrimental to its performance in unicast traffic."

Wireless ad-hoc networks are based on a cooperative communication model, where all nodes not only generate traffic but also help to route traffic from other nodes to its final destination. In such an environment where there is no infrastructure support the lifetime of the network is tightly coupled with the lifetime of individual nodes. Most of the devices that form such networks are battery-operated, and thus it becomes important to conserve energy so as to maximize the lifetime of a node. In this thesis, we present JTP, a new energy-aware transport protocol, whose goal is to reduce power consumption without compromising delivery requirements of applications. JTP has been implemented within the JAVeLEN system. JAVeLEN~\cite{javelen08redi}, is a new system architecture for ad hoc networks that has been developed to elevate energy efficiency as a first-class optimization metric at all protocol layers, from physical to transport. Thus, energy gains obtained in one layer would not be offset by incompatibilities and/or inefficiencies in other layers. To meet its goal of energy efficiency, JTP (1) contains mechanisms to balance end-to-end vs. local retransmissions; (2) minimizes acknowledgment traffic using receiver regulated rate-based flow control combined with selected acknowledgments and in-network caching of packets; and (3) aggressively seeks to avoid any congestion-based packet loss. Within this ultra low-power multi-hop wireless network system, simulations and experimental results demonstrate that our transport protocol meets its goal of preserving the energy efficiency of the underlying network. JTP has been implemented on the actual JAVeLEN nodes and its benefits have been demoed on a real system.

The nature and complexity of the security threats faced by our society in recent years have made it clear that a smart pervasive surveillance system constitutes the most effective cure, as it presents a conducive framework for seamless interaction between preventative capabilities and investigative protocols. Applications such as wild-life preserve monitoring, natural disaster warnings, and facility surveillance tend to be characterized by large and remote geographic areas, requiring large numbers of unattended sensor nodes to cover the volume-of-interest. Such large unattended sensor networks add new challenges as well as complicate the system management problem. Such challenges can be in the form of distributed operation with collaborative decision making, adaptive performance, and energy-aware strategies, to name a few. To meet the challenges of these mission-critical applications, the sensor system must exhibit capabilities such as heterogeneous and self-organized behaviour, data and information fusion, and collaborative resources control and management. Sensor Management (SM) refers to the process that plans and controls the use of the sensor nodes in a manner that synergistically maximizes the success rate of the whole system in achieving the goals of its mission in assessing the situation in a timely, reliable, and accurate fashion. Managing heterogeneous sensors involves making decisions and compromises regarding alternate sensing strategies under time and resource availability constraints. As a result, the performance of the collective sensors dictates the performance of the entire system. Consequently, there is a need for an intelligent Sensor Management Framework (SMF) to drive the system performance. SMF provides a control system to manage and coordinate the use of sensing resources in a manner that maximizes the system success rate in achieving its goals. An SMF must handle an overwhelming amount of information collected, and adapt to the highly dynamic environments, in addition to network and system limitations. This thesis proposes a resource-aware and intelligent SMF for managing pervasive sensor systems in surveillance context. The proposed SMF considerably improves the process of information acquisition by coordinating the sensing resources in order to gather the most reliable data from a dynamic scene while operating under energy constraints. The proposed SMF addresses both the operation of the coordination paradigm, as well as, the local and collaborative decision making strategies. A conceptual analysis of the SM problem in a layered structure is discussed to introduce an open and flexible design framework based on the service-oriented architecture to provide a modular, reusable, and extendable framework for the proposed SMF solution. A novel sensor management architecture, called Extended Hybrid Architecture for Sensor Management (E-HASM), is proposed. E-HASM combines the operation of the holonic, federated, and market-based architectures in a complementary manner. Moreover, a team-theoretic formulation of Belief-Desire-Intention (BDI), that represent the E-HASM components, is proposed as a mechanism for effective energy-aware decision making to address the local sensor utility. Also, intelligent schemes that provide adaptive sensor operation to the changes in environment dynamics and sensor energy levels are designed to include adaptive sleep, active sensing, dynamic sensing range, adaptive multimodality, and constrained communication. Furthermore, surveillance systems usually operate under uncertainty in stochastic environment. Therefore, this research formulates the collaborative decision-making entities as Partially Observable Markov Decision Processes (POMDP). To increase the tracking quality and the level of the information reliability, cooperation between the sensors is adopted, which adds an extra dimension in the design of the proposed SMFs. The propose SMF is implemented using the Jadex platform and is compared to the popular centralized architecture. The results illustrate the operation of the proposed SMF outperforms in terms of tracking quality, detection rate, energy consumption, network lifetime, and scalability.

Broadcasting is a communication model for a node to emit the packets via wireless channels to its neighbor nodes. In mobile ad hoc networks (MANETs), it is commonly implemented through flooding to find routes, send alarm signals and page a particular host. Conventionally, ad hoc routing protocols, such as AODV, use blind flooding extensively for on-demand route discovery, which could result in a high number of redundant retransmissions, leading to serious contention and collisions referred to as the broadcast storm problem. A gossip-based approach, in which each node forwards a message with some probability, has been proposed in past years to alleviate this problem. The approach combines gossiping with AODV (denoted as AODV+G) and exhibits a significant performance improvement in simulations. In this paper, we make a mathematical inference from observing the behavior of the gossip-based approach, and improve the gossip-based approach by employing the remaining energy of nodes in the gossip mechanism (denoted as AODV+GE) to extend the lifetime of the entire network and improve the packet delivery ratio. Through mathematical inference and simulations we show that AODV+GE outperforms AODV+G in terms of the lifetime of the whole network, average node energy consumption, and packet delivery ratio.

The nature and complexity of the security threats faced by our society in recent years have made it clear that a smart pervasive surveillance system constitutes the most effective cure, as it presents a conducive framework for seamless interaction between preventative capabilities and investigative protocols. Applications such as wild-life preserve monitoring, natural disaster warnings, and facility surveillance tend to be characterized by large and remote geographic areas, requiring large numbers of unattended sensor nodes to cover the volume-of-interest. Such large unattended sensor networks add new challenges as well as complicate the system management problem. Such challenges can be in the form of distributed operation with collaborative decision making, adaptive performance, and energy-aware strategies, to name a few. To meet the challenges of these mission-critical applications, the sensor system must exhibit capabilities such as heterogeneous and self-organized behaviour, data and information fusion, and collaborative resources control and management. Sensor Management (SM) refers to the process that plans and controls the use of the sensor nodes in a manner that synergistically maximizes the success rate of the whole system in achieving the goals of its mission in assessing the situation in a timely, reliable, and accurate fashion. Managing heterogeneous sensors involves making decisions and compromises regarding alternate sensing strategies under time and resource availability constraints. As a result, the performance of the collective sensors dictates the performance of the entire system. Consequently, there is a need for an intelligent Sensor Management Framework (SMF) to drive the system performance. SMF provides a control system to manage and coordinate the use of sensing resources in a manner that maximizes the system success rate in achieving its goals. An SMF must handle an overwhelming amount of information collected, and adapt to the highly dynamic environments, in addition to network and system limitations. This thesis proposes a resource-aware and intelligent SMF for managing pervasive sensor systems in surveillance context. The proposed SMF considerably improves the process of information acquisition by coordinating the sensing resources in order to gather the most reliable data from a dynamic scene while operating under energy constraints. The proposed SMF addresses both the operation of the coordination paradigm, as well as, the local and collaborative decision making strategies. A conceptual analysis of the SM problem in a layered structure is discussed to introduce an open and flexible design framework based on the service-oriented architecture to provide a modular, reusable, and extendable framework for the proposed SMF solution. A novel sensor management architecture, called Extended Hybrid Architecture for Sensor Management (E-HASM), is proposed. E-HASM combines the operation of the holonic, federated, and market-based architectures in a complementary manner. Moreover, a team-theoretic formulation of Belief-Desire-Intention (BDI), that represent the E-HASM components, is proposed as a mechanism for effective energy-aware decision making to address the local sensor utility. Also, intelligent schemes that provide adaptive sensor operation to the changes in environment dynamics and sensor energy levels are designed to include adaptive sleep, active sensing, dynamic sensing range, adaptive multimodality, and constrained communication. Furthermore, surveillance systems usually operate under uncertainty in stochastic environment. Therefore, this research formulates the collaborative decision-making entities as Partially Observable Markov Decision Processes (POMDP). To increase the tracking quality and the level of the information reliability, cooperation between the sensors is adopted, which adds an extra dimension in the design of the proposed SMFs. The propose SMF is implemented using the Jadex platform and is compared to the popular centralized architecture. The results illustrate the operation of the proposed SMF outperforms in terms of tracking quality, detection rate, energy consumption, network lifetime, and scalability.

Cloud computing enables the delivery of remote computing, software and storage services through web browsers following pay-as-you-go model. In addition to successful commercial applications, many research efforts including DOE Magellan Cloud project focus on discovering the opportunities and challenges arising from the computing and data-intensive scientific applications that are not well addressed by the current supercomputers, Linux clusters and Grid technologies. The elastic resource provision, noninterfering resource sharing and flexible customized configuration provided by the Cloud infrastructure has shed light on efficient execution of many scientific applications modeled as Directed Acyclic Graph (DAG) structured workflows to enforce the intricate dependency among a large number of different processing tasks. Meanwhile, the Cloud environment poses various challenges. Cloud providers and Cloud users pursue different goals. Providers aim to maximize profit by achieving higher resource utilization and users want to minimize expenses while meeting their performance requirements. Moreover, due to the expanding Cloud services and emerging newer technologies, the ever-increasing heterogeneity of the Cloud environment complicates the challenges for both parties. In this thesis, we address the workflow scheduling problem from different applications and various objectives. For batch applications, due to the increasing deployment of many data centers and computer servers around the globe escalated by the higher electricity price, the energy cost on running the computing, communication and cooling together with the amount of CO2 emissions have skyrocketed. In order to maintain sustainable Cloud computing facing with ever-increasing problem complexity and big data size in the next decades, we design and develop energy-aware scientific workflow scheduling algorithm to minimize energy consumption and CO2 emission while still satisfying certain Quality of Service (QoS) such as response time specified in Service Level Agreement (SLA). Furthermore, the underlying Cloud hardware/Virtual Machine (VM) resource availability is time-dependent because of the dual operation modes namely on-demand and reservation instances at various Cloud data centers. We also apply techniques such as Dynamic Voltage and Frequency Scaling (DVFS) and DNS scheme to further reduce energy consumption within acceptable performance bounds. Our multiple-step resource provision and allocation algorithm achieves the response time requirement in the step of forward task scheduling and minimizes the VM overhead for reduced energy consumption and higher resource utilization rate in the backward task scheduling step. We also evaluate the candidacy of multiple data centers from the energy and performance efficiency perspectives as different data centers have various energy and cost related parameters. For streaming applications, we formulate scheduling problems with two different objectives, namely one is to maximize the throughput under a budget constraint while another is to minimize execution cost under a minimum throughput constraint. Two different algorithms named as Budget constrained RATE (B-RATE) and Budget constrained SWAP (B-SWAP) are designed under the first objective; Another two algorithms, namely Throughput constrained RATE (TP-RATE) and Throughput constrained SWAP (TP-SWAP) are developed under the second objective.

Submitted by Irene Nascimento (irene.kessia@ufpe.br) on 2016-09-06T17:39:17Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) DissertacaoDeMestrado-IrineuMoura-imlm2.pdf: 1240260 bytes, checksum: 4bbaf8839fa3d5be7fca586e1f290f68 (MD5) / Made available in DSpace on 2016-09-06T17:39:17Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) DissertacaoDeMestrado-IrineuMoura-imlm2.pdf: 1240260 bytes, checksum: 4bbaf8839fa3d5be7fca586e1f290f68 (MD5) Previous issue date: 2015-08-24 / Energy consumption has been gaining traction as yet another major concern that mainstream software developers must be aware of. It used to be mainly the focus of hardware designers and low level software developers, e.g., device driver developers. Nowadays, however, mostly due to the ubiquity of battery-powered devices, any developer in the software stack must be prepared to deal with this concern. Thus, to be able to properly assist them and to provide guidance in future research it is crucial to understand how they have been handling this matter. This thesis aims to aid in this regard by exploring a set of software changes, i.e., commits, to obtain insights into actual solutions implemented by open source developers when dealing with energy consumption. We use as our main data source GITHUB, a source code hosting platform for collaborative development, and extract a sample of the available commits across several different projects. From this sample, we manually curate a set of energy-aware commits, that is, any commit that refers to a source code change where developers intentionally modify, or aim to modify, the energy consumption (or power dissipation) of a system or make it easier for other developers or end users to do so. We then apply a qualitative research method to extract recurring patterns of information and to group the commits that intend to save energy into categories. A small survey was also conducted to assess the quality of our analysis and to further expand our understanding of the changes. During our analysis we also cover different aspects of the commits. We observe that the majority of the changes (~47%) still target lower levels of the software stack, i.e., kernels, drivers and OS-related services, while application level changes encompass ~34% of them. We notice that developers may not always be certain of the energy consumption impact of their changes before actually performing them, among our dataset we identify several instances (~12%) of commits where developers show signs of uncertainty towards their change’s effectiveness. We also highlight the possible software quality attributes that may be favored over energy efficiency. Notably, we spot a few instances of commits where developers performed a change that would negatively impact the energy consumption of the system in order to fix a bug. It is also worth noting, we draw attention to a specific group of changes which we call "energy-aware interfaces". They add tuning knobs that can be used by developers or end users to control the energy consumption of an underlying component. / O controle do consumo de energia tem ganhado cada vez mais atenção como outro tipo de interesse ao qual desenvolvedores de software devem estar atentos. Antes esse tipo de preocupação era principalmente o foco de designers de hardware e desenvolvedores de baixonível, como por exemplo, desenvolvedores de drivers de dispositivos. Entretanto, devido à ubiquidade de dispositivos dependentes de bateria, qualquer desenvolvedor deve estar preparado para enfrentar essa questão. Logo, entender como eles estão lidando com o consumo de energia é crucial para estarmos aptos a auxiliá-los e para prover uma direção adequada para pesquisas futuras. Com o intuito de ajudar nesse sentido, essa tese explora um conjunto de mudanças de software, isto é, commits, para entender melhor sobre os tipos de soluções que são implementadas de fato por desenvolvedores de código aberto quando os mesmos devem lidar com o consumo de energia. Nós utilizamos o GITHUB como nossa principal fonte de dados, uma plataforma de hospedagem de código fonte para o desenvolvimento colaborativo de projetos de software, e extraímos uma amostra dos commits disponíveis entre vários projetos diferentes. Dessa amostra, nós manualmente selecionamos um conjunto de commits "energy-aware", isto é, qualquer commit que se refere a uma modificação de código onde o desenvolvedor propositalmente modifica, ou intenciona modificar, o consumo de energia (ou a dissipação de potência) de um sistema ou torna mais fácil para que outros desenvolvedores ou usuários finais possam fazê-lo. Nós então aplicamos sobre esses commits um método de análise qualitativa para extrair padrões recorrentes de informação e para agrupar os commits que intencionam reduzir o consumo energético em categorias. Uma pequena pesquisa também foi realizada com os autores dos commits para avaliar a qualidade da nossa análise e para expandir nosso entendimento sobre as modificações. Nós também consideramos diferentes aspectos dos commits durante a análise. Observamos que a maioria das modificações (~47%) ainda se aplicam às mais baixas camadas de software, isto é, kernels e drivers, enquanto que mudanças a nível de aplicação compreendem ~34% do nosso conjunto de dados. Nós notamos que os desenvolvedores nem sempre estão seguros do impacto de suas modificações no consumo de energia antes de realizá-las, em nosso conjunto de dados identificamos várias instâncias de modificações (~12%) em que os desenvolvedores demonstram sinais de incerteza em relação à eficácia de suas mudanças. Também apontamos alguns dos possíveis atributos de qualidade de software que são favorecidos em detrimento do consumo de energia. Entre essas, destacamos alguns commits onde os desenvolvedores realizaram uma modificação que impactaria negativamente no consumo de energia com o intuito de consertar algum problema existente no software. Também achamos interessante ressaltar um grupo específico de modificações que chamamos de “interfaces energy-aware”. Elas adicionam controles no software em questão que possibilitam outros desenvolvedores ou usuários finais a ajustar o consumo de energia de algum componente subjacente.

The ability to use Software Defined Radio (SDR) in the civilian mobile applications will make it possible for the next generation of mobile devices to handle multi-standard personal wireless devices and ubiquitous wireless devices. The original military standard created many beneficial characteristics for SDR, but resulted in a number of disadvantages as well. Many challenges in commercializing SDR are still the subject of interest in the software radio research community. Four main issues that have been already addressed are performance, size, weight, and power. This investigation presents an in-depth study of SDR inter-components communications in terms of total link delay related to the number of components and packet sizes in systems based on Software Communication Architecture (SCA). The study is based on the investigation of the controlled environment platform. Results suggest that the total link delay does not linearly increase with the number of components and the packet sizes. The closed form expression of the delay was modeled using a logistic function in terms of the number of components and packet sizes. The model performed well when the number of components was large. Based upon the mobility applications, energy consumption has become one of the most crucial limitations. SDR will not only provide flexibility of multi-protocol support, but this desirable feature will also bring a choice of mobile protocols. Having such a variety of choices available creates a problem in the selection of the most appropriate protocol to transmit. An investigation in a real-time algorithm to optimize energy efficiency was also performed. Communication energy models were used including switching estimation to develop a waveform selection algorithm. Simulations were performed to validate the concept.

In planning for future electricity supplies certain issues will need to be considered such as increased energy usage, urbanisation, reduction in personnel, global warming and the conservation of natural resources. As the result, some countries have investigated the transformation of their existing power grid to the so-called Smart Grid. The Smart Grid has three main characteristics which are, to some degree, antagonistic. These characteristics are the provision of good power quality, energy cost reduction and improvement in the reliability of the grid. The need to ensure that they can be accomplished together demands much richer Information and Communications Technology (ICT) networks than the current systems available. In this research we have identified the gap in the current proposals for the ICT of the power grid. We have designed and developed an ICT architecture for the neighbourhood sub-Grid level of the electrical network, where monitoring at this level is very underdeveloped because most current grids are controlled centrally and the response of the neighbourhood area is not generally monitored or actively controlled. Our designed ICT architecture, which is based on established architectural principles, can incorporate data from heterogeneous sources. This layered architecture provides both the sensors that can directly measure the electrical activity of the network (e.g. voltage) and also the sensors that measure the environment (e.g. temperature) since these provide information that can be used to anticipate demand and improve control actions. Additionally, we have de-signed a visualisation tool as an interface for a grid operators to facilitate a better comprehension of the behaviour of the neighbourhood level of the Smart Grid. Since we have noticed that energy aware ICT is a prerequisite for an efficient Smart Grid, we have utilised two different approaches to tackle this issue. The first approach was to utilise a cluster-based communication technique for the second layer of the architecture, which comprises Wireless Sensor Networks, where energy limitation is the major problem. Accordingly, we have analysed the energy-aware topology for wireless sensor networks embedded in the mentioned layer. We provide evidence that the proposed topology will bring energy efficiency to the communication network of the Smart Grid. The second approach was to develop a data reduction algorithm to reduce the volume of data prior to data transmission. We demonstrated that our developed data reduction is suitable for Smart Grid applications which can keep the integrity and quality of data. Finally, the work presented in this thesis is based on a real project that is being implemented in the medium voltage power network of the University of Manchester where power grid instrumentation, real data and professionals in the field are available. Since the project is long-term and the environmental sensor networks in particular are not currently installed we have evaluated some of our predictions via simulation. However, where the instrumentation was available, we were able to compare our predictions and our simulations with actual experimental results.