The Internet of Things (IoT) has led to rapid growth in smart cities. However, IoT botnet-based attacks against smart city systems are becoming more prevalent. Detection methods for IoT botnet-based attacks have been the subject of extensive research, but the identification of early-stage behaviour of the IoT botnet prior to any attack remains a largely unexplored area that could prevent any attack before it is launched. Few studies have addressed the early stages of IoT botnet detection using monolithic deep learning algorithms that could require more time for training and detection. We, however, propose an edge-based deep learning system for the detection of the early stages of IoT botnets in smart cities. The proposed system, which we call EDIT (Edge-based Detection of early-stage IoT Botnet), aims to detect abnormalities in network communication traffic caused by early-stage IoT botnets based on the modular neural network (MNN) method at multi-access edge computing (MEC) servers. MNN can improve detection accuracy and efficiency by leveraging parallel computing on MEC. According to the findings, EDIT has a lower false-negative rate compared to a monolithic approach and other studies. At the MEC server, EDIT takes as little as 16 ms for the detection of an IoT botnet.

We introduce IoTSimSecure, a novel IoT simulator addressing the gap of IoT simulators for dealing with cyber-security threats, specifically battery draining attacks occurring in real-time. This will serve as a valuable testbed for evaluating current AI-driven security strategies. IoTSimSecure aims to address these needs by offering an architecture that combines signature-based and anomaly-based detection methods.

This study offers SimulatorBridger, a simulator for IoT and Osmotic computations considering the Edge/Cloud continuum enabling connection performance evaluation and optimization by providing different configuration abilities. We contextualize the need for this simulator within the e-Health domain, for which the timely transmission of patient data collected by IoT devices is crucial for guaranteeing adequate treatments. The study addresses two primary research questions: whether it is sufficient to estimate real-time communication times by counting IoT device connections rather than tracking precise locations, and the impact of different Osmotic configurations on IoT packet transmission times. Our findings demonstrate that SimulatorBridger effectively determines the optimal Osmotic architecture configurations for efficient communication. The results illustrate the simulator's ability to accurately replicate communication patterns using both detailed mobility traces (MT) and a simplified Minimum Common Denominator (MCD) approach, which considers only the number of communications per time unit. The importance of appropriate MEL allocation policies in optimizing communication times is underscored, showcasing the simulator's potential to enhance the efficiency and reliability of IoT-based patient monitoring systems in healthcare scenarios.

Geo-distributed web-applications (GWA) can be deployed across multiple geographically separated datacenters to reduce the latency of access for users. Finding a suitable deployment for a GWA is challenging due to the requirement to consider a number of different parameters, such as host configurations across a federated infrastructure. The ability to evaluate multiple deployment configurations enables an efficient outcome to be determined, balancing resource usage while satisfying user requirements. We proposeGeoDeploy, a framework designed for finding a deployment solution for GWA. We evaluateGeoDeployusing both a formal algorithmic model and a practical cloud-based deployment. We also compare our approach with other existing techniques.

Propositionalisation tampers the running time of state-of-the-art algorithms in declarative temporal model mining, as they exhaustively generate the clauses instantiated with the results of frequent itemset mining algorithms. Existing algorithms also exploit non-indexed data representations, thus negatively affecting the overall running time. This paper proposes a novel temporal model mining algorithm, Bolt, twinning confidence and support metrics as heuristics for candidate pruning with data structures enabling fast temporal data scanning. Bolt outperforms both state-of-the-art and renditions of existing mining algorithms using KnoBAB as a library.

Personalised health and fitness provide users with information regarding their wellbeing and an opportunity to inform healthcare services for better patient outcomes. Underpinning this industry sector is the need to establish human activity recognition (HAR) in a ubiquitous manner. For example, through the use of smartwatches and/or mobile phones gathering information such as heart rates, movement, and steps of a user. The engineering challenge is providing accurate, informative, and timely data without rapidly depleting the mobile device's battery life. This problem is compounded as a number of algorithms used to process such data require substantial, cloud-based resources, to achieve higher accuracy. Therefore, a balance is required between battery depletion, accuracy of data, and timely delivery of results through a mixture of cloud and local algorithmic execution. In this article, we proposeAE-HAR (Accuracy and Energy Aware-HAR)model that delivers engineered solutions which approach optimal combinations in the consideration of energy consumption, accuracy, and timeliness of results.AE-HARintroduces a “light-weight”machine learningon-device component identifying the probabilistic accuracy of data together with energy consumption identification requirements. A heuristic is then adopted to determine if cloud-enabled calculations are required while including possible performance costs related to the analysis of networking infrastructures. Our model is validated in a real-world environment through experimentation that demonstrates accuracy in excess of 93% and energy consumption savings in excess of 94%.

We observe significant interest in reinforcement learning methods for real-world sensing-control scenarios driven by the sensor data streams. However, the delay introduced to the data by the communication channels may degrade the system's performance. It is especially crucial in the internet of things (IoT), where devices with constraint resources and low throughput networks are used.

State of the art for model checking exploit computationally intensive solutions, bottlenecked by either repeated data access or suboptimal algorithmic implementations. Our solution outperforms the previous solutions while proposing novel temporal logic operators for accessing relational tables.

Summary Advancement of computing and communication techniques transforms the traditional transport system into the intelligent transportation system (ITS). The development of distributed computing in a vehicular network platform also called Vehicular Edge Computing (VEC) promise to address most of the challenges faced by the ITS. Localization is important in these vehicular networks because of its key contribution in autonomous driving, smart traffic monitoring, and collision avoidance services. For localization, current GPS and hybrid methods are in‐efficient because of GPS outage in urban infrastructure and dynamic nature of the vehicular networks. The cooperative localization approaches, on the other hand, use dedicated short range communication to broadcast messages and estimate location. However, these messages are un‐encrypted and periodic which gives a privacy risk for vehicles. This article presents a privacy‐preserving cooperative localization in vehicular network based upon dynamic pseudonym changing strategy. First, the localization delay is addressed with the implementation of dynamic vehicular edge assignment for computational task management. In the next step, the localization is estimated from the neighbor and road side unit ranging measurement followed by a real‐time prediction of the vehicle. The performance of the proposed algorithms is analyzed in terms of localization accuracy and privacy preservation strength. Furthermore, the proposed method is simulated in a real city scenario followed by localization accuracy and privacy analysis. Finally, the localization accuracy and privacy strength of the proposed approach are compared with the state‐of‐the‐art methods.

Summary Containers are gaining popularity over virtual machines as they provide the advantages of virtualization with the performance of near bare metal. The uniformity of support provided by Docker containers across different cloud providers makes them a popular choice for developers. Evolution of microservice architecture allows complex applications to be structured into independent modular components making them easier to manage. High‐performance computing (HPC) applications are one such application to be deployed as microservices, placing significant resource requirements on the container framework. However, there is a possibility of interference between different microservices hosted within the same container (intracontainer) and different containers (intercontainer) on the same physical host. In this paper, we describe an extensive experimental investigation to determine the performance evaluation of Docker containers executing heterogeneous HPC microservices. We are particularly concerned with how intracontainer and intercontainer interference influences the performance. Moreover, we investigate the performance variations in Docker containers when control groups (cgroups) are used for resource limitation. For ease of presentation and reproducibility, we use Cloud Evaluation Experiment Methodology (CEEM) to conduct our comprehensive set of experiments. We expect that the results of evaluation can be used in understanding the behavior of HPC microservices in the interfering containerized environment.

Internet of Things (IoT) allows the creation of virtually endless connections into a global array of distributed intelligence. However, the design, development, and deployment of IoT applications are complex and complicated due to various unwarranted challenges. For instance, addressing the IoT application users' subjective and objective opinions with IoT workflow instances remains a challenge for the design of a more holistic approach. Moreover, the complexity of IoT applications increased exponentially due to the heterogeneous nature of the Edge/Cloud services, utilised with the aim of lowering latency in data transformation and increase re-usability. Hence, in this paper, we present an IoT workflow composition system (IoTWC) to allow IoT users to pipeline their workflows with proposed IoT workflow activity abstract patterns. IoTWC leverages the analytic hierarchy process (AHP) to compose the multi-level IoT workflow that satisfies the requirements of any IoT application. Moreover, the users are befitted with recommended IoT workflow configurations using an AHP based multi-level composition framework. The proposed IoTWC is validated on a user case study to evaluate the coverage of IoT workflow activity abstract patterns and a real-world scenario for smart buildings. The comprehensive analysis shows the effectiveness of IoTWC in terms of IoT workflow abstraction and composition.

In this paper we propose a solution to delivering scalable realtime physics simulations. Although high performance computing simulations of physics related problems do exist, these are not realtime and do not model the real-time intricate interactions of rigid bodies for visual effect common in video games (favouring accuracy over real-time). As such, this paper presents the first approach to real-time delivery of scalable, commercial grade, video game quality physics. This is achieved by taking the physics engine out of the player's machine and deploying it across standard cloud based infrastructures. The simulation world is then divided into sections that are then allocated to servers. A server maintains the physics for all simulated objects in its section. Our contribution is the ability to maintain a scalable simulation by allowing object interaction across section boundaries using predictive migration techniques. We allow each object to project an aura that is used to determine object migration across servers to ensure seamless physics interactions between objects. The validity of our results is demonstrated through experimentation and benchmarking. Our approach allows player interaction at any point in real-time (influencing the simulation) in the same manner as any video game. We believe that this is the first successful demonstration of scalable real-time physics.

A case study of a computer games engineering course is presented.The course has been designed with close input from industry and is achieving a high rate of success in the number of graduates being recruited by the target industry.The organisers of the course have extensive experience in both the software engineering industry, and in delivering academic teaching.These experiences are combined so that commercial software development practices, technologies and philosophies are adopted throughout the delivery of the academic course.The paper discusses the specifics of how and why this was achieved, and uses the Game Engineering course as an exemplar for encouraging the adoption of commercially inspired techniques within the teaching of software engineering and computer science more generally.

Modern smartphones feature multiple applications which access shared data on the solid state storage within the device. As applications become more complex, contention over this memory resource is becoming an issue. This leads to increased battery drain as the applications are forced to touch the solid state device repeatedly after failing to retrieve or store data due to contention from other applications. We describe an optimistic concurrency control algorithm, combining a novel Read-Write-Validate phase sequence with virtual execution. The protocol is suitable for governing transactions operating on databases residing on resource-constrained devices. Increasing energy efficiency and reducing latency are primary goals for our algorithm. We show that this is achieved by reducing persistent store access, and satisfy real-time requirements via transaction scheduling that affords greater determinism.

This paper reviews developments in general purpose computing on graphics processor units (GPGPU computing) from the perspective of video-game-related artificial intelligence (AI). We present an overview of the field, beginning with early shader language solutions and continuing to discuss three accessible platforms for GPGPU development: CUDA, OpenCL, and Direct Compute. Consideration is given to the commercial and practical realities which hinder the adoption of GPGPU solutions within video game AI, and developments in GPGPU computing directly relevant to common AI practices within the video games industry are reviewed in depth.

SUMMARY Within Internet‐based service environments, distributed monitoring mechanisms face challenges with respect to the diverse communication protocols, application languages, and monitoring requirements that exist. Creation of monitoring solutions on a per‐service basis becomes time‐consuming and misses opportunities to reuse existing logic. Ideally the deployment of monitoring solutions would be simple. Simplicity could be achieved by freeing monitoring solutions from domain dependencies, promoting the ability to use automatically generated monitoring code and support for the automated deployment of monitoring components. Work is presented here that progresses these ambitions by providing a generic, distributed monitoring and evaluation framework based on Metric Collector (MeCo) components. MeCo components form part of a unified monitoring framework which can interpret Service Level Agreements to automatically provide tailored service monitoring. Once deployed, these components can gather measurement data across a range of service technologies as used within E‐Commerce services, with minimal impact upon observed service environments. Copyright © 2011 John Wiley & Sons, Ltd.

This paper describes work to ease the resource allocation problem in the domain of game server hosting. A solution was sought that required no alteration to game server code and would not inhibit a player's gaming experience. Although an academic work, the problem is tackled in a commercial setting.

Three-tier middleware architecture is commonly used for hosting enterprise-distributed applications. Typically, the application is decomposed into three layers: front end, middle tier, and back end. Front end ("Web server") is responsible for handling user interactions and acts as a client of the middle tier, while back end provides storage facilities for applications. Middle tier ("application server") is usually the place where all computations are performed. One of the benefits of this architecture is that it allows flexible management of a cluster of computers for performance and scalability; further, availability measures, such as replication, can be introduced in each tier in an application-specific manner. However, incorporation of availability measures in a multitier system poses challenging system design problems of integrating open, nonproprietary solutions to transparent failover, exactly once execution of client requests, nonblocking transaction processing, and an ability to work with clusters. This paper describes how replication for availability can be incorporated within the middle and back-end tiers, meeting all these challenges. This paper develops an approach that requires enhancements to the middle tier only for supporting replication of both the middleware back-end tiers. The design, implementation, and performance evaluation of such a middle-tier-based replication scheme for multidatabase transactions on a widely deployed open source application server (JBoss) are presented.

In a distributed virtual environment interest management schemes attempt to ensure virtual world objects exchange messages only if they are interacting. This provides an opportunity to allow such systems to scale to support many hundreds, even thousands, of virtual world objects by reducing the need to send and process unnecessary messages. However, arriving at the optimum configuration for the interest management scheme is a challenging and complex problem. The customary approach is to configure parameters in an ad-hoc manner (supported by experience). However, such an approach is unlikely to yield optimum system performance. Here we propose a means of finding optimal parameter values by way of a simulation tool equipped with evolutionary optimization capabilities. We provide a series of experiments that indicate that our simulator can aid in configuring interest management schemes to gain better system performance.

We describe approaches for satisfying the real-time collision detection requirements of distributed virtual environments. We assume a distributed virtual environment is deployed using client/server architecture typical of commercial massive multiplayer online games. We exploit the scalability provided by the clustering of servers in the development of a real-time collision detection service that may scale to satisfy the requirements of virtual environments that are constructed of many thousands of objects. We present performance figures that show our approaches to be scalable in that an addition of servers to a cluster results in an increased number of objects that can be considered for collision detection in real-time.

In this paper we present an implementation of an interest management scheme using standard message oriented middleware (MOM) technologies to provide scalable message dissemination for networked games. The aim of all interest management schemes is to identify when objects that inhabit a virtual world should be interacting and to enable such interaction via message passing while preventing objects that should not be interacting from exchanging messages. The time taken by existing interest management schemes to resolve which objects influence each other may be too large to enable the desired interaction to occur. Furthermore, existing interest management implementations tend to be proprietary and are built directly on top of networking protocols. In this paper we present an approach to interest management based on the predicted movement of objects. Our approach determines the frequency of message exchange between objects on the likelihood that such objects will influence each other in the near future. We then demonstrate, via implementation and experimentation, how existing middleware standards provide a suitable platform for the deployment of our interest management scheme.

We present an approach to collision detection that is appropriate for satisfying the requirements of interest management schemes used in distributed virtual environments. Such environments are characterized by their distributed deployment over a number of nodes connected via a computer network. The aim of an interest management scheme is to identify when objects that populate a simulation supported by a distributed virtual environment (objects could be hosted on different nodes) should be interacting via message exchange while preventing objects that should not be interacting from exchanging messages. The approach to collision detection presented in this paper produces accurate results when determining object interactions. Furthermore, we present variations on our approach that exploit any coherence that may exist in a simulation to provide a solution that may scale for large numbers of objects.

A recent trend has seen the extension of object-oriented middleware. A major advantage components offer over objects is that only the business logic of an application needs to be addressed by a programmer with support services required incorporated into the application at deployment time. This is achieved via components (business logic of an application), containers that host components and are responsible for providing the underlying middleware services required by components and application servers that host containers. Well-known examples of component middleware architectures are Enterprise Java Beans (EJBs) and the CORBA Component model (CCM). Two of the many services available at deployment time in most component architectures are component persistence and atomic transactions. This paper examines, using EJBs, how replication for availability can be supported by containers so that components that are transparently using persistence and transactions can also be made highly available.

The paper develops a distributed system architecture and proposes an implementation framework for conducting dependable Internet based online auctions, meeting the requirements of scalability and service integrity. Current auction services essentially rely on a central auction server. Given the increasing popularity and usage of electronic auctions, such a centralised approach is fundamentally restrictive with respect to scalability. Further, different national markets have different monetary regulations and may employ different procedures for payment settlements. Catering for local market autonomy means that decentralisation is an essential and practical requirement. With these design goals in mind, the paper presents a system architecture and a framework for a fault-tolerant implementation of the architecture.

Distributed applications should be able to make use of an object group service in a number of application specific ways. Three main modes of interactions can be identified; (i) request-reply; a client issues a request to multiple servers and waits for their replies; this represents a commonly occurring scenario when a service is replicated; (ii) group-to-group request-reply; a generalisation of the previous case, where clients are themselves groups; and (iii) Peer Participation: here all the members are regularly multicasting messages (asynchronous invocation); this represents a commonly occurring scenario when the purpose of an application is to share information between members, (e.g., a teleconferencing application). Customisation within each class of interaction is frequently required for obtaining better performance. This paper describes the design and implementation of a flexible CORBA object group service that supports the three types of interactions and enables application specific customisation. Performance figures collected over low latency LAN and high latency WAN are presented to support the case for flexibility.