Predicting the future location of mobile objects reinforces location-aware services with proactive intelligence and helps businesses and decision-makers with better planning and near real-time scheduling in different applications such as traffic congestion control, location-aware advertisements and monitoring public health and well-being. Recent developments in smartphone and location sensors technology and the prevalence of using location-based social networks alongside the improvements in AI and machine learning techniques provide an excellent opportunity to exploit massive amounts of historical and real-time contextual information to recognise mobility patterns and achieve more accurate and intelligent predictions. This unique survey provides a comprehensive overview of the next useful location prediction problem with context-awareness and the related studies. First, we explain the concepts of context and context-awareness and define the next location prediction problem. Then we analyse more than 30 studies in this field concerning the prediction method, the challenges addressed, the datasets and metrics used for training and evaluating the model and the types of context incorporated. Finally, we discuss the advantages and disadvantages of different approaches, focusing on the usefulness of the predicted location and identifying the open challenges and future work on this subject.

In Smart City applications, urban mobility involves complex interactions between traffic infrastructure, diverse road users, and physical environment. This paper addresses the limitations of conventional scene modeling methods that often fail to capture the varied and volatile nature of urban road scenes, particularly in representing the dynamic situations that unfold within them. Inaccurate situation representation hinders precise depiction of scene evolution, limiting our ability to understand and respond effectively to complex urban situations. This paper addresses these challenges by presenting the novel concept of the Context-Aware Scene Graph (CSG) for representing situations used in reasoning applications for enhancing safety and efficiency in urban environments, particularly for bicycle riders. CSG integrates multi-modal data, including ontological knowledge, sensor data, and images, to provide a comprehensive representation of urban road situations, enabling informed decision-making. This paper also validates the effectiveness of the proposed approach using real- world IoT datasets and camera images, with a focus on the bicycle dooring use case. The results outperform existing scene modeling methods by accurately representing situations, including those previously overlooked. The approach also ensures consistent representation, completeness, and captures transitions between situations, including causal relations. These findings highlight our approach's effectiveness in improving road safety, efficiency, and urban life quality through enhanced scene understanding.

Predicting situations in real-time applications is non-trivial. Fusing and incorporating the plethora of heterogeneous context information from many sources in the ecosystem that a user resides in to derive their situation is an expensive and time-consuming process. Yet context is useful only when a user can effectively make use of it in time and reliably. In this paper, using a proactive cyclist hazard alerting scenario, we propose a mechanism to proactively cache context information, so that cyclists are alerted of impending hazards before they might even occur. Our novel approach, which is capable of caching reliable predictive context information has significantly reduced the time to deliver context by 91% and the cost by 80%. We ensure the reliability of predictive cached context using a cross-verification routine that the false-positive rate tends to zero. The context cache is structured hierarchically such that our novel proactive context caching mechanism is capable of caching all low-level to high-level pieces of context, unlike any previous approaches.

Mobile Crowd Computing (MCC) leverages the collaborative power of nearby devices to solve resource-intensive tasks, offering transformative potential across various fields. However, ensuring device reliability—which directly impacts the trustworthiness of devices in MCC environments—poses a significant challenge. Balancing validation accuracy with performance and energy efficiency is particularly difficult due to MCC’s dynamic and resource-constrained decentralized nature. Existing validation methods are not feasible in MCC, as they can negatively affect speed and energy consumption. This paper introduces the Honeybee-RS framework, a novel approach for validating offloaded computational results in MCC environments. Honeybee-RS provides delegator-based validation mechanisms and derives reliability scores from the validation process. Experiments demonstrate the effectiveness of these mechanisms, significantly enhancing reliability in MCC while maintaining performance.

The research of architecture has tremendous significance in realizing quantum Internet. Although there is not yet a standard quantum Internet architecture, the distributed architecture is one of the possible solutions, which utilizes quantum repeaters or dedicated entanglement sources in a flat structure for entanglement preparation & distribution. In this paper, we analyze the distributed architecture in detail and demonstrate that it has three limitations: 1) possible high maintenance overhead, 2) possible low-performance entanglement distribution, and 3) unable to support optimal entanglement routing. We design a hierarchical quantum Internet architecture and a communication model to solve the problems above. We also present a W-state Based Centralized Entanglement Preparation & Distribution (W-state Based CEPD) scheme and a Centralized Entanglement Routing (CER) algorithm within our hierarchical architecture and perform an experimental comparison with other entanglement preparation & distribution schemes and entanglement routing algorithms within the distributed architecture. The evaluation results show that the entanglement distribution efficiency of hierarchical architecture is 11.5% higher than that of distributed architecture on average (minimum 3.3%, maximum 37.3%), and the entanglement routing performance of hierarchical architecture is much better than that of a distributed architecture according to the fidelity and throughput.

Real-time applications increasingly rely on context information to provide relevant and dependable features. Context queries require large-scale retrieval, inferencing, aggregation, and delivery of context using only limited computing resources, especially in a distributed environment. If this is slow, inconsistent, and too expensive to access context information, the dependability and relevancy of real-time applications may fail to exist. This paper argues, transiency of context (i.e., the limited validity period), variations in the features of context query loads (e.g., the request rate, different Quality of Service (QoS), and Quality of Context (QoC) requirements), and lack of prior knowledge about context to make near real-time adaptations as fundamental challenges that need to be addressed to overcome these shortcomings. Hence, we propose a performance metric driven reinforcement learning based adaptive context caching approach aiming to maximize both cost- and performance-efficiency for middleware-based Context Management Systems (CMSs). Although context-aware caching has been thoroughly investigated in the literature, our approach is novel because existing techniques are not fully applicable to caching context due to (i) the underlying fundamental challenges and (ii) not addressing the limitations hindering dependability and consistency of context. Unlike previously tested modes of CMS operations and traditional data caching techniques, our approach can provide real-time pervasive applications with lower cost, faster, and fresher high quality context information. Compared to existing context-aware data caching algorithms, our technique is bespoken for caching context information, which is different from traditional data. We also show that our full-cycle context lifecycle-based approach can maximize both cost- and performance-efficiency while maintaining adequate QoC solely based on real-time performance metrics and our heuristic techniques without depending on any previous knowledge about the context, variations in query features, or quality demands, unlike any previous work. We demonstrate using a real world inspired scenario and a prototype middleware based CMS integrated with our adaptive context caching approach that we have implemented, how realtime applications that are 85% faster can be more relevant and dependable to users, while costing 60.22% less than using existing techniques to access context information. Our model is also at least twice as fast and more flexible to adapt compared to existing benchmarks even under uncertainty and lack of prior knowledge about context, transiency, and variable context query loads.

This paper presents a wireless data collection frame-work that employs an unmanned aerial vehicle (UAV) to efficiently gather data from distributed IoT sensors deployed in a large area. Our approach takes into account the non-zero communication ranges of the sensors to optimize the flight path of the UAV, resulting in a variation of the Traveling Salesman Problem (TSP). We prove mathematically that the optimal waypoints for this TSP-variant problem are restricted to the boundaries of the sensor communication ranges, greatly reducing the solution space. Building on this finding, we develop a low-complexity UAV-assisted sensor data collection algorithm, and demonstrate its effectiveness in a selected use case where we minimize the total energy consumption of the UAV and sensors by jointly optimizing the UAV's travel distance and the sensors' communication ranges.

Context-awareness (CA) has become an evolving trend, especially in the domain of Internet of Things (IoT). With the progress of IoT, the necessity for accessing real-time contextual information has become a critical factor for the advancement of IoT applications. Context management platforms (CMPs) have been proposed in the literature to support the needs of such Context-aware IoT applications. However, there are still significant gaps in terms of supporting the increasing needs of Context-aware applications, including the performance analysis of CMPs. In this paper, we propose a scene-graph based approach to generate context queries which primarily intends to support the performance analysis of CMPs and its ability to support plethora of Context-aware IoT application needs. Given the situation driven nature of IoT applications, the ability to generate relevant queries needs to be very realistic. Hence, we propose a novel Situation State Machine based approach to capture and model real-world situations. To demonstrate the potential to generate relevant context queries based on dynamic situations, a bicycle dooring use case is considered. We then present a template-based query generation approach to create realistic queries that represent real-world IoT application environment. The dooring use case is considered to validate the ability to represent complex queries, and the ability to generate complex queries in linear time.

Context-awareness is becoming more relevant for smarter modern-day applications. With billions of IoT devices able to monitor a plethora of parameters in near real-time, inferring contextual information at scale while maintaining adequate Quality of Context and delivering in time has been non-trivial for state-of-the-art centralized Context Management Systems. Further, handling complex situations and entity relations based on local awareness are areas that still need investigation. In this paper, we propose a novel edge-computing based architecture for distributed contextual intelligence systems that could address these research problems. First, we critically evaluate the current state-of-the-art in context-awareness and establish the necessity of a distributed architecture. Then, our proposed architecture is introduced along with the protocols and algorithms accompanied by real-world examples. The paper also highlights the future direction for research work in the area.

This paper devises a multi-agent Mission Coordinating Architecture (MCA) to achieve continuous situational awareness (SA) in bushfires, which can help with quick detection and accurate response to the hazards. In this paper, we use Unmanned Aerial Vehicles (UAVs) as instantiations of physical agents. MCA is a scalable architecture and aims to provide the UAVs with parallel mission plans, adopting a fire spread probability map and the Fuzzy C-means method to avoid mission overlap and duplicate information. The architecture is then enhanced by integrating a synchronized communication framework to facilitate UAVs’ adaptive cooperation and total flight time optimization. Furthermore, integrating the communication framework minimizes the number of deployed UAVs to fully cover the same area, saving considerable cost and energy compared to the Parallel Mode. The scalability challenge of the Parallel Mode, determining the required number of UAVs to cover the entire area, and the efficiency of the mission planning algorithm are thoroughly investigated and compared to the performance of the Communication Mode. Finally, the simulation results prove the MCA’s effectiveness in enhancing the UAVs’ exploration capability, resulting in comprehensive monitoring of all affected areas. Note to Practitioners—The proposed architecture in this research could offer flexibility to increase the number of UAVs on demand without requiring a change and adjustment of the parameters. Furthermore, a synchronized communication framework enhances MCA, enabling all UAVs to share their resources and exploit residual battery time to assist each other, manage the overall operation time, and reduce the total operation cost.

Context awareness adds intelligence to and enriches data for applications, services and systems while enabling underlying algorithms to sense dynamic changes in incoming data streams. Context-aware machine learning is often adopted in intelligent services by endowing meaning to Internet of Things(IoT)/ubiquitous data. Intelligent transportation systems (ITS) are at the forefront of applying context awareness with marked success. In contrast to non-context-aware machine learning models, context-aware machine learning models often perform better in traffic prediction/classification and are capable of supporting complex and more intelligent ITS decision-making. This paper presents a comprehensive review of recent studies in context-aware machine learning for intelligent transportation, especially focusing on road transportation systems. State-of-the-art techniques are discussed from several perspectives, including contextual data (e.g., location, time, weather, road condition and events), applications (i.e., traffic prediction and decision making), modes (i.e., specialised and general), learning methods (e.g., supervised, unsupervised, semi-supervised and transfer learning). Two main frameworks of context-aware machine learning models are summarised. In addition, open challenges and future research directions of developing context-aware machine learning models for ITS are discussed, and a novel context-aware machine learning layered engine (CAMILLE) architecture is proposed as a potential solution to address identified gaps in the studied body of knowledge.

Mobile devices are ubiquitous, heterogeneous and resource constrained. Execution of complex tasks in mobile devices are resource demanding and time-consuming, forcing developers to offload portions of the complex task to cloud or edge computing resources. Task offloading becomes increasingly challenging due to intermittent Internet connectivity, remote resource unavailability, high costs, latency, and limited energy of the mobile device. A mobile device user is typically surrounded by other mobile devices, which can be leveraged to collaboratively compute a resource-intensive task. With the help of a work sharing framework, it is feasible for devices to communicate and collaborate. However, some mobile devices are incapable of computing complex portions of the task, and some can compute in accelerated mode. In this demonstration, we introduce Honeybee-T a collaborative mobile crowd computing framework that uses a work-stealing algorithm. The algorithm allows work sharing with collaborating devices based on devices' computational ability and task-dependencies. The experiments show that by employing Honeybee-T framework, when compared to monolithic execution of a large compute-intensive task, there is a considerable performance gain, as well as energy savings.

Smart energy management based on the Internet of Things (IoT) aims to achieve optimal energy utilization through real-time energy monitoring and analyses of power consumption patterns in IoT networks (e.g., residential homes and offices) supported by wireless technologies - this is of great significance for the sustainable development of energy. Energy disaggregation is an important technology to realize smart energy management, as it can determine the power consumption of each appliance from the total load (e.g., aggregated data). Also, it gives us clear insights into users’ daily power-consumption-related behaviours, which can enhance their awareness of power-saving and lead them to a more sustainable lifestyle. This paper reviews the state-of-the-art algorithms for energy/power disaggregation and public datasets of power consumption. Also, potential use cases for smart energy management based on IoT networks are presented along with a discussion of open issues for future study.

This paper explores a possible use-case of creating an integrated multiuser-multidevice interaction (2MUDI) model in IoT collectives, in particular, in an aged care centre environment. A prototype has been designed and developed, which has given a name KATE. The system comprises Internet-connected robot(s), multiple mobile devices and multiple users. Family members of the seniors admitted to aged care centres can monitor the seniors via the robot. Staff members, including doctors and nurses, who look after these seniors can also interact with and use the robot(s). This data can also be accessible by family members via an application on their mobile devices. This work has modelled complex interactions and considered the implementation challenges, societal implications in a 2MUDI system and demonstrate its applicability with the KATE system.

With the proliferation of Internet of Things (IoT) devices, there is a demand for technologies to support high-velocity, dynamic resource provisioning to provide secure, cost-efficient, and real-time IoT services in resource-constrained environments. Conventional fog computing by itself cannot address such requirements and needs to be complemented with opportunistic fog computing, by providing mobile fog resources on-demand. In this paper, we discuss key issues in this area, and investigate potential solutions from existing work. We conclude this paper with a summary of gaps, and propose an opportunistic architecture for future work.

As mobile devices evolve to be powerful and pervasive computing tools, their usage also continues to increase rapidly. However, mobile device users frequently experience problems when running intensive applications on the device itself, or offloading to remote clouds, due to resource shortage and connectivity issues. Ironically, most users’ environments are saturated with devices with significant computational resources. This paper argues that nearby mobile devices can efficiently be utilised as a crowd-powered resource cloud to complement the remote clouds. Node heterogeneity, unknown worker capability, and dynamism are identified as essential challenges to be addressed when scheduling work among nearby mobile devices. We present a work-sharing model, called Honeybee, using an adaptation of the well-known work stealing method to load balance independent jobs among heterogeneous mobile nodes, able to accommodate nodes randomly leaving and joining the system. The overall strategy of Honeybee is to focus on short-term goals, taking advantage of opportunities as they arise, based on the concepts of proactive workers and opportunistic delegator. We evaluate our model using a prototype framework built using Android and implement two applications. We report speedups of up to four with seven devices and energy savings up to 71 percent witheight devices.

Human activity recognition using embedded mobile and embedded sensors is becoming increasingly important. Scaling up from individuals to groups, that is, Group Activity Recognition (GAR), has attracted significant attention recently. This article proposes a model and modeling language for GAR called GroupSense-L and a novel distributed middleware called GroupSense for mobile GAR. We implemented and tested GroupSense using smartphone sensors, smartwatch sensors, and embedded sensors in things, where we have a protocol for these different devices to exchange information required for GAR. A range of continuous group activities (from simple to fairly complex) illustrates our approach and demonstrates the feasibility of our model and richness of the proposed specialization. We then conclude with lessons learned for GAR and future work.

In this paper, we investigate a cooperative car parking mechanism called CoPark-WS, for finding parking spaces in large areas. It is a decentralised approach which relies on vehicle-to-vehicle communications. There are two parameters we consider here in parking search behavior, namely, searching time and walking distance. The type of strategic cooperation and rational decision-making involved in selecting the search target areas in CoPark-WS are demonstrated via extensive simulations, which also show the robust performance of CoPark-WS in different circumstances.

Crowdsourcing has become an important problem-solving technique, for both traditional and mobile applications. There have been work on crowdsourcing particular database operations to humans, and work on spatial crowdsourcing or mobile crowdsourcing, where the workers are mobile device users. This paper presents LogicCrowd which extends logic programming, commonly used for knowledge-based applications, with crowdsourcing capabilities by adding operators to connect to crowdsourcing platforms, thereby enabling crowdsourcing via logic programs and crowd-powered knowledge-based applications. In LogicCrowd, we also introduce a novel unification approach called crowd unification that automatically leverages human knowledge for comparisons through the crowdsourcing paradigm. In addition, LogicCrowd is built on a Prolog platform running on Android mobile devices, thereby enabling logic-based spatial and mobile crowdsourcing. Our proposed method for combining rule-based reasoning and crowdsourcing via LogicCrowd programs is demonstrated in a range of scenarios. Because energy is an important consideration for mobile platforms, we also investigate the energy characteristics of the crowdsourcing operators in our LogicCrowd prototype. Our experiments show the relationships between crowdsourcing operations and energy consumption, and illustrate the factors influencing energy consumption when using crowdsourcing.

The rapid propagation of computer virus is one of the greatest threats to current cybersecurity. This work deals with the optimal control problem of virus propagation among computers and external devices. To formulate this problem, two control strategies are introduced: (a) external device blocking, which means prohibiting a fraction of connections between external devices and computers, and (b) computer reconstruction, which includes updating or reinstalling of some infected computers. Then the combination of both the impact of infection and the cost of controls is minimized. In contrast with previous works, this paper takes into account a state-based cost weight index in the objection function instead of a fixed one. By using Pontryagin’s minimum principle and a modified forward-backward difference approximation algorithm, the optimal solution of the system is investigated and numerically solved. Then numerical results show the flexibility of proposed approach compared to the regular optimal control. More numerical results are also given to evaluate the performance of our approach with respect to various weight indexes.

Human activity recognition using embedded mobile and embedded sensors is becoming increasingly important. Scaling up from individuals to groups, that is, group activity recognition, has attracted significant attention recently. This paper proposes a model and specification language for group activities called GroupSense-L, and a novel architecture called GARSAaaS (GARSA-as-a-Service) to provide services for mobile Group Activity Recognition and Situation Analysis (GARSA) applications. We implemented and evaluated GARSAaaS which is an extension of a framework called GroupSense where sensor data, collected using smartphone sensors, smartwatch sensors and embedded sensors, are aggregated via a protocol for these different devices to share information, as required for GARSA. We illustrate our approach via a scenario for providing services for tour leaders aiding Vehicle-to-Human (V2H), Vehicle-to-Group (V2G) and Vehicle-to-Vehicle (V2V) interactions to increase the group safety. We demonstrate the feasibility of our model and expressiveness of our proposed model.

We demonstrate decentralized car parking using vehicle-to-vehicle cooperation. Extensive simulations of car parking scenarios illustrate the effectiveness of the approach in reducing car park searching time and getting nearer parking spaces.

Despite1 the increase in drone popularity, still, there are numerous issues to be solved. But there are many beneficial applications that can be derived through the use of drones combined with other available, scalable and growing technologies such as mobile devices, smartwatches, and add-on electronic sensors. In this paper, we propose and investigate an approach to using drones acting as servers (edge computing style) in order to collect data, provide Internet access, and process data for mobile users.

Internet of things (IoT) while providing virtually unlimited opportunities, is also prone to many "risks". Risk analysis is therefore a mandatory action for any IoT system to ensure security, privacy, performance and reliability. Moreover, the complexity and structural dynamism of these mobile cloud/IoT systems further adds to the need for specialized, systematic and automated risk analysis and mitigation as well as the context awareness of risk management. This paper represents a part of our ongoing work towards creating a quantitative context-aware risk management system, suitable for dynamic mobile cloud and IoT environments, which would use the current context parameters to predict/identify potential risks and where possible, manage risks autonomously.

Human activity recognition using mobile sensors is becoming increasingly important. Scaling up from individuals to groups, that is, Group Activity Recognition (GAR), has attracted significant attention recently. This paper investigates energy consumption for GAR and proposes a novel distributed middleware called GroupSense for mobile GAR. We implemented and tested GroupSense, which incorporates a protocol for the exchange of information required for GAR. We also investigated the battery drain of continuous activity recognition in a range of simple GAR scenarios. We then conclude with lessons learnt for GAR.

Pervasive and mobile computing technologies can make our everyday living environments and our cities "smart", i.e., capable of reaching awareness of physical and social processes and of dynamically affecting them in a purposeful way. In general, living in a smart environment and being made part of its activities somehow make us - as individuals - smarter as well, by increasing our perceptory and social capabilities. However, a potential risk could be to start delegating too much to the environment itself, losing in critical attention, abandoning individual decision making for relying on collective computational governance of our activity, and in the end also losing awareness of environmental and social processes. The panel intends to discuss the above issues with the help of relevant researchers in the area of pervasive computing, smart environments, collective intelligence.

Internet of Things (IoT) represents a cyber-physical world where physical things are interconnected on the Web. This paper presents an architecture designed for Energy-efficient Inter-organizational wireless sensor data collection Framework (EnIF). Environmental monitoring and urban sensing are two major application scenarios in IoT. Different from the traditional sensor environments, environmental sensing in IoT may require battery-powered nodes to perform the sensing tasks. Such a requirement raises a critical challenge to ensure that sensor data gathering can be collected in a timely and energy-efficient manner. Although numerous energy-efficient approaches for IoT scenarios have been proposed, previous works assumed the entire network was managed by a single organization in which the network establishment and communication have been pre-configured. This assumption is inconsistent with the fact that IoT is established in a federated network with heterogeneous devices controlled by different organizations. The aim of the framework is to enable a dynamic inter-organizational collaborative topology towards saving energy from data transmissions using a service-oriented architecture.

This paper presents the La Trobe e-Sanctuary concept, which aims to develop a cross-reality environment where a physical wildlife sanctuary is synchronised in real-time with its virtual counterpart. We outline the concept, research issues in realising this concept, components of our on-going project, and applications.

With the proliferation of mobile devices, and their increasingly powerful embedded processors and storage, vast resources increasingly surround users. We have been investigating the concept of on-demand ad hoc forming of groups of nearby mobile devices in the midst of crowds to cooperatively perform computationally intensive tasks as a service to local mobile users, or what we call mobile crowd computing. As devices can vary in processing power and some can leave a group unexpectedly or new devices join in, there is a need for algorithms that can distribute work in a flexible manner and still work with different arrangements of devices that can arise in an ad hoc fashion. In this article, we first argue for the feasibility of such use of crowd-embedded computations using theoretical justifications and reporting on our experiments on Bluetooth-based proximity sensing. We then present a multilayered work-stealing style algorithm for distributing work efficiently among mobile devices and compare speedups attainable for different topologies of devices networked with Bluetooth, justifying a topology-flexible opportunistic approach. While our experiments are with Bluetooth and mobile devices, the approach is applicable to ecosystems of various embedded devices with powerful processors, networking technologies, and storage that will increasingly surround users.

Group Activity Recognition (GAR) is a challenging research area in context-aware computing which has attracted much attention recently. Many studies have been conducted in the field of activity recognition (AR) along with their applications in domains such as health, smart homes, daily living and life logging. However, still many open issues exist. Lack of an energy-efficient approach is one of the most vital issues in the context of AR. GAR work often suffers from energy consumption issues for the reason that, apart from AR process, there is the requirement to have more interaction among members of the group and a need to run more complex recognition processes. Moreover, almost all work in GAR are technology-oriented and assume that our real-life environment remains fixed once the system has been established, but this may not be the case. Hence, we propose a framework called Group Sense for GAR towards addressing these issues. Also, a relatively simple scheme for GAR, with a protocol for the exchange of information required for GAR, has been implemented, tested and evaluated. We then conclude with lessons learnt for GAR.

Peer-to-Peer technologies have been widely used in networks which manage vast amount of data daily. The proliferation of mobile devices strongly motivates mobile peer-to-peer network (M-P2P) applications, with benefits from network effects. We argue that logic programming for crowd sourcing can be useful in peer-to-peer computing for querying and multicasting tasks shared over peer networks. We introduce a declarative crowd sourcing platform for mobile applications, which combines conventional machine computation and the power of the crowd in social networking, particularly in M-P2P networks. This paper discusses a simple extension of Prolog, which we call Logic Crowd, focusing on enabling goal evaluation over peers in mobile peer networks. Additionally, we demonstrate that logic programming for crowd sourcing can be useful in peer-to-peer computing for querying and P2P style of task sharing over short-range networks. In this paper, we illustrate the potential of our approach via programming idioms, a prototype implementation and scenarios.

Traffic congestion becomes a cascading phenomenon when vehicles from a road segment chaotically spill on to successive road segments. Such uncontrolled dispersion of vehicles can be avoided by evenly distributing vehicles along alternative routes. This paper proposes a practical multiagent-based approach, which is designed to achieve acceptable route allocation within a short time frame and with low communication overheads. In the proposed approach, which is called Congestion Avoidance and Route Allocation using Virtual Agent Negotiation (CARAVAN), vehicle agents (VAs) in the local vicinity communicate with each other before designated decision points (junctions) along their route. Cooperative route-allocation decisions are performed at these junctions. VAs use intervehicular communication to propagate key traffic information and undertake its distributed processing. Every VA exchanges its autonomously calculated route preference information to arrive at an initial allocation of routes. The allocation is improved using a number of successive virtual negotiation “deals.” The virtual nature of these deals requires no physical communication and, thereby, reduces communication requirements. In addition to the theory and concept, this paper presents the design and implementation methodology of CARAVAN, including experimental results for synthetic and real-world road networks. Results show that when compared against the shortest path algorithm for travel time improvements, CARAVAN offers 21%-43% gain (when traffic demand is below network capacity) and 13%-17% gain (when traffic demand exceeds network capacity), demonstrating its ability to regulate overall system traffic using local coordination strategies.

In this paper, we propose and develop a platform to support data collection for mobile crowdsensing from mobile device sensors that is under-pinned by real-time mobile data stream mining. We experimentally show that mobile data mining provides an efficient and scalable approach for data collection for mobile crowdsensing. Our approach results in reducing the amount of data sent, as well as the energy usage on the mobile phone, while providing comparable levels of accuracy to traditional models of intermittent/continuous sensing and sending. We have implemented our Context-Aware Real-time Open Mobile Miner (CAROMM) to facilitate data collection from mobile users for crowdsensing applications. CAROMM also collects and correlates this real-time sensory information with social media data from both Twitter and Facebook. CAROMM supports delivering real-time information to mobile users for queries that pertain to specific locations of interest. We have evaluated our framework by collecting real-time data over a period of days from mobile users and experimentally demonstrated that mobile data mining is an effective and efficient strategy for mobile crowdsensing.

Mixing the virtual world and the physical world seamlessly has become a phenomenal mobile service to users, providing new directions in pervasive computing. There are many variants for this mix such as Physical Annotations (PAs) and Mixed Reality (MR). There are many uses of PAs and MR such as education, entertainment, shopping, tourism and more. Many platforms have been developed to provide this service. However, there is still no standard or formal definition for PAs which aims to consolidate and extend the possibilities with PAs. The aim of this paper is to explore the concept of the Physical Annotation and to provide a formal model for it. The paper analyzes and applies the formal model to some of the existing major systems that are used for PAs. Then, we propose a generic PA system architecture and illustrate our model using a scenario concerning a shopping center.

In this paper, we introduce the concept of Easy Computer Interaction (ECI), and describe our design and implementation of an ECI application prototype using a collection of IR distance sensors. Performance and limitations of the application are analyzed.

In rapidly growing transportation networks, traffic congestion can result from inefficient traffic control infrastructure or ineffective traffic control measures. Existing congestion management techniques in Intelligent Transportation Systems (ITS) have not been very effective due to lack of autonomous and collaborative behavior of the constituent traffic control entities involved in these techniques. Moreover, these entities cannot easily adapt to the traffic dynamics and the traffic control intelligence is mostly centralised making it susceptible to overload and failures. The autonomous and distributed nature of multi-agent systems is well-suited to the transportation domain which is dynamic and geographically distributed. This paper reviews existing congestion management techniques and discusses their limitations. The paper, further, comprehensively surveys multi-agent techniques for congestion management in ITS and describes their advantages over other existing techniques. The paper classifies the multi-agent techniques based on the locus of decision control intelligence and focuses on their suitability of application in congestion management. We conclude with outstanding issues and challenges.

This paper proposes the idea of providing artifacts with specifications of their situation transforming behaviour. Such specifications can be created by designers (and so would come together with the artifact on purchase) or end-users to implement smart things that transform their environment in meaningful ways. The specifications are based on situation semantics first developed for studies in linguistics.

A smart space is a physical space such as a seminar room or a university campus that is richly and invisibly interwoven with sensors and actuators into everyday objects, and consists of connected devices. This space is often difficult to use and its capability is often invisible from users' awareness, especially for those who are unfamiliar with this space. The difficulty results from the overload of features/configurations offered by individual devices and their combinations while the invisibility comes from the blend of computational elements into the environment. This paper describes TaskRec, a new system that automatically generates a list of high-level tasks supported within a smart space based on user's location, surrounding devices, and user's pointing gestures. TaskRec may automatically execute or guide the user through the accomplishment of the selected task based on the corresponding task model.

The case for integrating software agent and web service paradigms has been well documented, and we believe that convergence of these two paradigms, enhanced with context awareness, can enable more efficient and effective pervasive services. Software agents in service-oriented environments have traditionally been limited to either using, providing, or aggregating services. We propose that in dynamic heterogeneous environments it would be sometimes beneficial if the agent, in addition to invoking remote services, could acquire the capacity to execute functionality provided by the service and run it locally. To this end, we build a performance analysis model that compares time consumption and network load of service access with that of component use. We argue that such a model would allow an agent to dynamically select the more efficient alternative. We present a multicriteria decision-making model that helps dynamic selection, describe experiments comparing the two approaches, and discuss results and lessons learned.

What if physical artifacts or devices can be aware of each others' physical presence and location, and interact with each other without user intervention or to enable innovative applications? We propose a model for devices to manage awareness of each other, extending a spatial model of interaction previously used in virtual environments. While there has been previous work on cooperative artifacts, our model is unique in introducing a quantitative technique. Moreover, our model is novel in adding to proximity-based interactions among devices the concepts of the following: 1) aura collision types based on relative locations of devices and 2) multiple (adjustable) levels of awareness and concealment measures so that each device can control how much it wants to be aware of others and how much it wants to be concealed from others. Our model is general and supports awareness of devices in (sufficiently) close physical proximity and the right aura sizes. Such devices' awareness of each other facilitates or triggers interaction, and normally precedes interaction among devices (as in human communication). Our model has numerous applications, from smart soft-toy features to proximity-triggered data exchanges.

We envision an intelligent environment comprising collections of smart artifacts, each artifact with an embedded processor, networking and sensing capabilities. The interactive capabilities of the environment are due to the collective working of the smart artifacts. This position paper proposes a rule-based declarative programming model for programming intelligent environment behaviours involving a collection of smart artifacts. We outline our language, provide examples and highlight issues. We contend that a peer-to-peer architecture forms a useful approach to building (and extending, over time) intelligent environments, i.e., by adding (perhaps incrementally over time, a few artifacts at a time) cooperative programmable smart artifacts to physical environments.

In resource-constrained devices, adaptation of data stream processing to variations of data rates and availability of resources is crucial for consistency and continuity of running applications. However, to enhance and maximize the benefits of adapta

We propose the EADRM (Explanation-Aware Distributed Reputation Management) framework for sharing web services’ reputation in heterogeneous environments. This framework advocates rationale extraction for meaningful exchange of reputation and includes a decision support algorithm for combining rationale-fortified recommendations.

Software agents roaming around and accessing services is an important part of the vision of the Semantic Web. The need to engage in diverse activities in rapidly changing environments makes it essential that these agents are able to adapt to varying situations. We propose a novel approach to engineer adaptive software agents for such scenarios. Our agents have the ability to exchange their capabilities with peers, support multiple forms of adaptation, enable software reuse through a component-based infrastructure and provide fine-grained and efficient agent mobility. We describe our solution, the first implementation and identify further research issues.

This paper explores the use of rationale for understanding the context of reputation information so as to facilitate the exchange and transfer of reputation information across distributed heterogeneous reputation systems for selection of the best service for a particular user's requirements.

This paper proposes a multiagent based virtual community as a new means to support pervasive computing services. We give a conceptual definition of the concept of Place-Based Virtual (PBV) Community. A PBV-community can be used in an environment of context-aware applications and is where agents can help users' interactions with services within the PBV- community. We have proposed a model for a PBV- Community and implemented a prototype called "student digital assistant" based on the agent-oriented approach. We have developed a multiagent architecture and protocols to realize the PBV- community. Agents in this architecture can cooperate and collaborate to provide different services to end users based on the user's context.

Embedded with sensors and appropriate computational entities, a road intersection can be viewed as a pervasive computing environment. The crash rate in road intersections demonstrates the need for a fast and accurate collision detection system. We suggest that an intersection collision detection system should be able to adapt to different types of intersections for faster collision detection. Moreover, a real-time application-level communication protocol to warn affected drivers is required. An intersection agent that takes vehicular status information from vehicle agents and learns, detects and warns collisions at a road intersection is proposed. The issues, challenges, and cost of a multiagent collision avoidance system are discussed. A communication protocol that is designed specifically with intersection safety in mind is presented here.

In open heterogeneous context-aware pervasive computing systems, suitable context models and reasoning approaches are necessary to enable collaboration and distributed reasoning among agents. This paper proposes, develops, and demonstrates the following: 1) a novel context model and reasoning approach developed with concepts from the state-space model, which describes context and situations as geometrical structures in a multidimensional space; and 2) a context algebra based on the model, which enables distributed reasoning by merging and partitioning context models that represent different perspectives of computing entities over the object of reasoning. We show how merging and reconciling different points of view over context enhances the outcomes of reasoning about the context. We develop and evaluate our proposed algebraic operators and reasoning approaches with cases using real sensors and with simulations. We embed agents and mobile agents with these modeling and reasoning capabilities, thus facilitating context-aware and adaptive mobile agents operating in open pervasive environments.

Reducing amount of data transmitted enables conserving scarce battery power in wireless sensor networks. In our previous work, we propose two data approximation algorithms for data reduction in sensor networks, maintaining the accuracy of query results within certain bounds. In this paper, we provide a performance study and analysis of these algorithms with emphasis on the types of data for which the algorithms are appropriate. We experimented with different data sets to determine the reduction ratios achieved , energy consumed, errors introduced, complexity of query answering obtained. We provide comparison of our algorithms with related methods. The presented results indicate the superiority of our methods in terms of data reduction and accuracy of query results.

We are addressing the problem of query processing in large sensor networks. Each sensor produces a large amount of streaming data and it may not be possible to store all the data. By caching some of the data in aggregated format, we will be able to answer queries referring to past data. We are investigating a hierarchical caching model where summarized data is cached. The granularity of aggregation becomes coarser as we move up the levels, starting from the actual data of the immediate past stored at the lowest level. We categorize queries into two types: queries that can be answered exactly and those that can be answered approximately. We have provided an analysis of the conditions under which the exact and approximate answers can be provided for a given query. When the query answer is approximate, an estimation of the error is provided.

Smart spaces should not be bound by physical constraints but should transcend them in order to provide enhanced services. One way of overcoming this is by having smart virtual counterparts represent the real world entities in smart spaces. We propose that mobile agent technology is aptly suited for providing smart virtual counterparts of real-world entities in the virtual world and can be used to enhance the performance of smart spaces. We also present a prototype implementation using the Grasshopper mobile agent toolkit as proof of concept.

We present AgentUDM a mobile agent based support infrastructure for ubiquitous data mining (UDM). The focus of AgentUDM is to address the issues of resource-constraints and disconnections with respect to UDM.

The selection of Web services is typically based on both functional and nonfunctional attributes of the service, such as the quality of service (QoS) levels. Reputation, a widely acknowledged nonfunctional QoS attribute is currently expressed as the average of user ratings given to the service. However, this expression confines reputation to the subjective perception of the end user and is limited by the lack of an objective representation of performance history. In this paper, we address the need for a reputation mechanism that couples the subjective perception of the end user with the objective view of performance history. To represent performance history, we propose a novel QoS metric termed verity. Verity measures the degree of consistency exhibited by the service provider in delivering the quality levels laid out in the service contract, over a range of previous transactions. We express reputation as a composition of user rating, the compliance levels exhibited by the provider and the verity value. We contend that this reputation expression is a more viable attribute of quality than user rating alone.

As Web service technology evolves, the idea of context-aware services gains more interest. An idea is that different sets of services will dynamically drop into the mobile users' devices depending on their contexts. To do this effectively requires location modelling and representation as well as spontaneity in downloading and executing the service interface on a mobile device. This paper introduces the concept and an implementation of hanging services that supports proactive and ad hoc context-aware services in mobile environments. This system works on top of an 802.11b wireless network. The prototype implementation is done using Web services and highly compact mobile code applications using Microsoft .NET compact framework.

We characterize situations as constraints on sensor readings expressed in rules. We also introduce an extension of Prolog which we call LogicCAP for programming context-aware applications, where situations are first-class entities. The operator "in-situation" in the language captures a common form of reasoning in context-aware applications, which is to ask if an entity is in a given situation. We show the usefulness of our approach via programming idioms, including defining relations among situations and integration with the Web.

This paper presents a hybrid distributed data mining (DDM) model for optimization of response time. The model combines a mobile agent approach with client server strategies to reduce the overall response time. The hybrid model proposes and develops accurate a priori estimates of the computation and communication components of response time as the costing strategy to support optimization. Experimental evaluation of the hybrid model is presented.

We present an approach to using the skeleton paradigm for grid computing, where the skeletons are executed by mobile agents. The skeletons we use are based on the Bird-Meertens Formalism, involving higher-order operations over data types.

A priori estimation of quality of service (QoS) levels is a significant issue in e-services since service level agreements (SLAs) need to specify and adhere to such estimates. Response time is an important metric for data intensive e-services such as data mining, data analysis and querying/information retrieval from large databases where the focus is on the time taken to present results to clients. A key component of response time in such data intensive services is the time taken to perform the computation, namely, the time taken to perform either data mining, analysis or retrieval. In this paper, we present an approach for accurately estimating the computation times of data intensive e-services.

Recent advances in mobile devices create a need for computing architectures and applications which are able to react to environmental changes in order to adapt to the changing context of computation. To date insufficient attention has been paid to the issues of defining an open component-based architecture which is able to describe complex computational context and handle different types of adaptation for a variety of new and existing pervasive enterprise applications. In this paper an architecture for pervasive enterprise systems is proposed. The architecture uses a component based modelling paradigm and an event-based mechanism which provides significant flexibility in dynamic system configuration and adaptation. The architecture includes context management which captures descriptions of complex user, device and application context including enterprise roles and role policies, and allows easy extension by new types of context. The architecture provides an open approach to adaptation which allows easy extension with adaptation mechanisms. In addition, the coordination language used to coordinate system events provides the flexibility needed in pervasive computing applications to support dynamic reconfiguration and a variety of communication paradigms.

LogicWeb mobile code consists of Prolog-like rules embedded in Web pages, thereby adding logic programming behaviour to those pages. Since LogicWeb programs are downloaded from foreign hosts and executed locally, there is a need to protect the client from buggy or malicious code. A security model is crucial for making LogicWeb mobile code safe to execute. This paper presents such a model, which supports programs of varying trust levels by using different resource access policies. The implementation of the model derives from an extended operational semantics for the LogicWeb language, which provides a precise meaning of safety.