Energy-efficient peer-to-peer networking for constrained-capacity mobile environments

Abstract Energy efficiency is a powerful measure for promoting sustainability in technological evolution and ensuring feasible battery life of end-user devices in mobile computing. Peer-to-peer technology provides decentralized and self-organizing architecture for distributing content between devices in networks that scale up almost infinitely. However, peer-to-peer networking may require lots of resources from peer nodes, which in turn may lead to increased energy consumption on mobile devices. For this reason, peer-to-peer networking has so far been considered unfeasible for mobile environment. This thesis makes several contributions towards enabling energy-aware peer-to-peer networking in mobile environments. First, an empirical study is conducted to understand the energy consumption characteristics of radio interfaces and typical composition of traffic in structured peer-to-peer networks. This is done in order to identify the most essential obstacles for utilizing peer-to-peer technology in mobile environments. Second, the e-Aware model for estimating the energy consumption of a mobile device is developed and empirically verified to achieve 3-21% error in comparison to real-life measurements. Third, the e-Mon model for the energy-aware load monitoring of peer nodes is developed and demonstrated to improve the battery life of mobile peer nodes up to 470%. Fourth, the ADHT concept of mobile agent based virtual peers is proposed for sharing the peer responsibilities between peer nodes in a subnet so that they can participate in a peer-to-peer overlay without compromising their battery life. The results give valuable insight into implementing energy-efficient peer-to-peer systems in mobile environments. The e-Aware energy consumption model accelerates the development of energy-efficient networking solutions by reducing the need for time-consuming iterations between system development and evaluations with real-life networks and devices. The e-Mon load monitoring model facilitates the participation of battery-powered devices in peer-to-peer and other distributed networks by enabling energy-aware load balancing where energy-critical mobile nodes carry less load than other nodes. The ADHT facilitates the participation of constrained-capacity wireless devices, such as machine-to-machine nodes, in a peer-to-peer network by allowing them to sleep for most of their time.Tiivistelmä Energiatehokkuus on kustannustehokas tapa vähentää päätelaitteiden käytön aiheuttamia kasvihuonepäästöjä sekä parantaa niiden akunkestoa. Vertaisverkkoteknologia tarjoaa hajautetun, itseorganisoituvan, sekä lähes rajattomasti skaalautuvan verkkoarkkitehtuurin päätelaitteiden väliseen tallennustilan, mediasisältöjen ja tietoliikennekapasiteetin suorajakamiseen. Vertaisverkkojen suurin heikkous mobiilikäytön näkökulmasta on niiden päätelaitteille aiheuttama ylimääräinen kuormitus, mikä näkyy lisääntyneenä energiankulutuksena. Tässä väitöskirjassa on tutkittu mekanismeja vertaisverkon päätelaitteiden energiatehokkuuden parantamiseksi, tavoitteena riittävä akunkesto mobiilikäytössä. Aluksi työssä tutkittiin empiirisesti langattomien verkkojen mobiilipäätelaitteille aiheuttamaa energiankulutusta sekä rakenteellisten vertaisverkkojen vertaispäätelaitteille aiheuttamaa liikennekuormitusta. Tavoitteena oli muodostaa käsitys suurimmista haasteista mobiililaitteiden käytölle vertaisverkoissa. Seuraavaksi mobiiliverkkojen energiankulutusta koskevasta havaintoaineistosta muodostettiin energiankulutusmalli, e-Aware, jolla voitiin arvioida mobiilipäätelaitteen energiankulutusta erilaisilla verkon liikenneprofiileilla. Mallilla saavutettiin parhaimmillaan kolmen prosentin keskimääräinen virhe. Kolmannessa vaiheessa kehitettiin energiatietoinen kuormanseurantamalli, e-Mon, jota käyttäen saavutettiin jopa 470 % lisäys mobiilin vertaispäätelaitteen akunkestoon. Viimeisessä vaiheessa kehitettiin ADHT-konsepti, joka on uudentyyppinen tapa jakaa vertaispäätelaitteiden kuormaa usean saman verkkoklusterin päätelaitteen kesken käyttäen laitteesta toiseen kiertävää mobiiliagenttia. Väitöskirjan tulokset osoittavat että mobiililaitteiden energiatehokkuutta vertaisverkoissa pystytään olennaisesti parantamaan energiatietoisia kuormanjakomekanismeja käyttäen. Työssä kehitetty e-Aware nopeuttaa energiatehokkaiden hajautettujen järjestelmien kehitystyötä tarjoamalla tehokkaan työkalun järjestelmän energiankulutuksen arvioimiseen jo kehitysvaiheessa. e-Mon mahdollistaa energiatietoisen kuormanjaon vertaisverkoissa tarjoamalla tarvittavan kuormanseurantamallin. ADHT puolestaan tarjoaa uudenlaisen tavan vähentää vertaisverkkojen aiheuttamaa päätelaitekuormitusta hyödyntäen maksimaalisesti rajoitetun kapasiteetin laitteiden unitilojen käyttöön perustuvaa energiankulutusoptimointia

Securing edge services for future smart healthcare and industrial IoT applications

Abstract Secure and intelligent environments are crucial for fostering future IoT applications such as digital healthcare and Industry 4.0. Such smart environments must enable needed digital services to the respective users ubiquitously and fulfill critical requirements such as ensuring security, privacy, and low latency. This paper summarizes the dissertation work [1] through three major contributions, i) a lightweight biometrics-based user authentication mechanism in the smart and gadget-less healthcare environment, ii) a conceptual three-tier mechanism for secure nodes bootstrapping and secure users access for digital services, and iii) a Blockchain and Edge computing based network architecture for IIoT use case to fulfill the needed requirements such as low-latency, trust management, and security among others. The performance evaluation of the proposed framework is carried out, and the obtained results highlight valuable insight of this work for enabling a secure future hyperconnected environment for various applications

Weathering the reallocation storm:large-scale analysis of edge server workload

Abstract Efficient service placement and workload allocation methods are necessary enablers for the actively studied topic of edge computing. In this paper, we show that under certain circumstances, the number of superfluous workload reallocations from one edge server to another may grow to a significant proportion of all user tasks — a phenomenon we present as a reallocation storm. We showcase this phenomenon on a city-scale edge server deployment by simulating the allocation of user task workloads in a number of scenarios capturing likely edge computing deployments and usage patterns. The simulations are based on a large real-world data set of city-wide Wi-Fi network connections in 2013—2014, with more than 47M connections over ca. 800 access points. We identify the conditions for avoiding the reallocation storm for three common edge-based reallocation strategies, and study the latency-workload trade-off related to each strategy. As a result, we find that the superfluous reallocations vanish when the edge server capacity is increased above a certain threshold, unique for each reallocation strategy, peaking at ca. 35% of top ES workload. Further, while a reallocation strategy aiming to minimize reallocation distance consistently resulted in the worst reallocation storms, the two other strategies, namely, a random reallocation strategy, and a bottom-up strategy which always chooses the edge server with the lowest workload as a reallocation target, behave nearly identically in terms of latency as well as the reallocation storm in dense edge deployments. Since the random strategy requires much less coordination, we recommend it over the bottom-up one in dense ES deployments

Resource-aware dynamic service deployment for Local IoT edge computing:healthcare use case

Abstract Edge Computing is a novel computing paradigm moving server resources closer to end-devices. In the context of IoT, Edge Computing is a centric technology for enabling reliable, context-aware and low-latency services for several application areas such as smart healthcare, smart industry and smart cities. In our previous work, we have proposed a three-tier IoT Edge architecture and a virtual decentralized service platform based on lightweight microservices, called nanoservices, running on it. Together, these proposals form a basis for virtualizing the available local computational capacity and utilizing it to provide localized resource-efficient IoT services based on the applications’ need. Furthermore, locally-deployed functions are resilient to access network problems and can limit the propagation of sensitive user data for improved privacy. In this paper, we propose an automatic service and resource discovery mechanism for efficient on-the-fly deployment of nanoservices on local IoT nodes. As use case, we have selected a healthcare remote monitoring scenario, which requires high service reliability and availability in a highly dynamic environment. Based on the selected use case, we propose a real-world prototype implementation of the proposed mechanism on Raspberry Pi platform. We evaluate the performance and resource-efficiency of the proposed resource matching function with two alternative deployment approaches: containerized and non-containerized deployment. The results show that the containerized deployment is more resource-efficient, while the resource discovery and matching process takes approximately 6–17 seconds, where containerization adds only 1–1.5 seconds. This can be considered a feasible price for streamlined service management, scalability, resource-efficiency and fault-tolerance

Evaluation of machine learning techniques for security in SDN

Abstract Software Defined Networking (SDN) has emerged as the most viable programmable network architecture to solve many challenges in legacy networks. SDN separates the network control plane from the data forwarding plane and logically centralizes the network control plane. The logically centralized control improves network management through global visibility of the network state. However, centralized control opens doors to security challenges. The SDN control platforms became the most attractive venues for Denial of Service (DoS) and Distributed DoS (DDoS) attacks. Due to the success and inevitable benefits of Machine Learning (ML) in fingerprinting security vulnerabilities, this article proposes and evaluates ML techniques to counter DoS and DDoS attacks in SDN. The ML techniques are evaluated in a practical setup where the SDN controller is exposed to DDoS attacks to draw important conclusions for ML-based security of future communication networks

Energy consumption analysis of high quality multi-tier wireless multimedia sensor network

Abstract Video surveillance is one of the promising applications of the Internet of Things paradigm. We see heterogeneous deployment of sensor platforms in a multi-tier network architecture as a key enabler for energy optimization of battery powered high-quality video surveillance applications. In this paper, we propose a heterogeneous wireless multimedia sensor network (WMSN) prototype composed of constrained low-power scalar sensor nodes and single board computers (SBCs). Whereas constrained nodes are used for preliminary motion detection, more capable SBCs are used as camera nodes. The camera nodes stream full HD (1080 pixels) video to a remote laptop during occurrence of an event (when motion is detected). We also present a simple power model and simulation results of battery life of the motes for variable event interval and event duration

sleepyCAM:power management mechanism for wireless video-surveillance cameras

Abstract In this paper, we propose an energy efficient motion detection and power management mechanism, called sleepyCAM, for wireless camera sensor nodes that do not otherwise support low-power modes. In the proposed solution, a low-power sensor node accompanied with a Pyroelectric Infrared (PIR) sensor and a relay is used to detect motion and manage the power usage of a high-power and high-resolution camera sensor node. To validate our work, we used two baseline benchmarks for comparison that are commonly used as motion detection mechanisms on wireless surveillance cameras: (a) hardware based motion detection using a PIR sensor and (b) software based motion detection using video frame comparison. The main contributions of this paper are the prototype implementation of the sleepyCAM, the surveillance application and the comparison of power consumption between the proposed and the baseline methods. The measurement results indicate that the power consumption of a surveillance camera node can be reduced significantly with the proposed mechanism

Performance and efficiency optimization of multi-layer IoT edge architecture

Abstract The recent IoT applications set strict requirements in terms of latency, scalability, security and privacy. The current IoT systems, where computation is done at data centers, provide typically very high computational and storage capacity but long routes between computational capacity and sensors/actuators make them unsuitable for latency-critical applications and services. Mobile Edge Computing (MEC) can address these problems by bringing computational capacity within or next to the base stations of access networks. Furthermore, to cope with access network problems, the capability of providing the most critical processes at the local network layer is also important. Therefore, in this paper, we compare the traditional cloud-IoT model, a MEC-based edge-cloud-IoT model, and a local edge-cloud-IoT model with respect to their performance and efficiency, using iFogSim simulator. The results complement our previous findings that utilizing the three-tier edge-IoT architecture, capable of optimally utilizing the computational capacity of each of the three tiers, is an effective measure to reduce energy consumption, improve end-to-end latency and minimize operational costs in latency-critical IoT applications

An overview of the security landscape of virtual mobile networks

Abstract 5G enables the use of different types of services over the same physical infrastructure through the concepts and technologies of virtualization, softwarization, network slicing and cloud computing. Mobile Virtual Network Operators (MVNOs), using these concepts, provide an opportunity to share the same physical infrastructure among multiple operators. Each MVNO can have own distinct operating and support systems. However, the technologies used to enable such an environment have their own explicit security challenges and solutions. The integrated environment built upon these novel concepts and technologies, thus, will have complex security implications and requirements to be satisfied. In this vain, this article provides an overview of the security challenges and potential solutions for MVNOs

A dark and stormy night:reallocation storms in edge computing

Abstract Efficient resource usage in edge computing requires clever allocation of the workload of application components. In this paper, we show that under certain circumstances, the number of superfluous workload reallocations from one edge server to another may grow to a significant proportion of all user tasks—a phenomenon we present as a reallocation storm. We showcase this phenomenon on a city-scale edge server deployment by simulating the allocation of user task workloads in a number of scenarios capturing likely edge computing deployments and usage patterns. The simulations are based on a large real-world data set of city-wide Wi-Fi network connections, with more than 47M connections over ca. 560 access points. We study the occurrence of reallocation storms in three common edge-based reallocation strategies and compare the latency–workload trade-offs related to each strategy. As a result, we find that the superfluous reallocations vanish when the edge server capacity is increased above a certain threshold, unique for each reallocation strategy, peaking at ca. 35% of the peak ES workload. Further, while a reallocation strategy aiming to minimize latency consistently resulted in the worst reallocation storms, the two other strategies, namely a random reallocation strategy and a bottom-up strategy which always chooses the edge server with the lowest workload as a reallocation target, behave nearly identically in terms of latency as well as the reallocation storm in dense edge deployments. Since the random strategy requires much less coordination, we recommend it over the bottom-up one in dense ES deployments. Moreover, we study the conditions associated with reallocation storms. We discover that edge servers with the very highest workloads are best associated with reallocation storms, with other servers around the few busy nodes thus mirroring their workload. Further, we identify circumstances associated with an elevated risk of reallocation storms, such as summertime (ca. 4 times the risk than on average) and on weekends (ca. 1.5 times the risk). Furthermore, mass events such as popular sports games incurred a high risk (nearly 10 times that of the average) of a reallocation storm in a MEC-based scenario