Leaf wetness is an important input parameter into disease prediction models. The use of machine learning algorithms for the classification of leaf wetness measurements from 30 meteorological stations in North Western Europe during the period of January 2014 to October 2018 was assessed in this study. The accuracy of the empirical models utilised within in this study was enhanced by increasing the relative humidity threshold from 90% to 92%. Increasing the relative humidity threshold led to an average increase in the classification accuracy of 1.12%. The use of machine learning classification algorithms consistently provided more accurate results for the prediction of leaf wetness when compared to the empirical models that were studied with an average increase in the classification accuracy of 4.85%. The sub-division of the data into regional subsets had a greater effect on the accuracy of the models than the temporal sub-division of the data. Machine learning classification techniques performed well compared with previously established empirical models for the prediction of leaf wetness. Further improvements in the algorithms are possible, making the techniques studied here a viable research tool.

Recent developments in Internet of Things have heightened the need for data sharing across application domains to foster innovation. As most of these IoT deployments are based on heterogeneous sensor types, there is increased scope for sharing erroneous, inaccurate or inconsistent data. This in turn may lead to inaccurate models built from this data. It is important to evaluate this data as it is collected to establish its quality. This paper presents an analysis of data quality as it is represented in Internet of Things (IoT) systems and some of the limitations of this representation. The paper then introduces the use of trust as a heuristic to drive data quality measurements. Trust is a well-established metric that has been used to determine the validity of a piece or source of data in crowd sourced or other unreliable data collection techniques. The analysis extends to detail an appropriate framework for representing data quality within the big data model. To demonstrate the application of a trust backed framework, we used data collected from a IoT deployment of sensors to measure air quality in which a low cost sensor was co-located with a gold reference sensor. Using data streams modeled based on a dataset from an IoT deployment, our initial results show that the framework's trust score are consistent with the accuracy measure of the machine learning models.

Rough K-Means (RKM) is an efficient clustering algorithm for overlapping datasets, and has captured increasing attention in recent years. RKM algorithms are the main focus on the further description of uncertain objects located in boundary regions in order to improve the performance. However, most available RKM algorithms fail to pay attention to the influence of imbalanced clusters, together with imbalanced spatial distributions (i.e., the cluster density) and differing cluster sizes (i.e., the number of object ratios). This paper seeks to address this deficiency and examines in detail some adverse effects caused by imbalanced clusters. To mitigate adverse effects of imbalanced clusters and decrease the computational cost, an interval type-2 fuzzy local measure for the RKM clustering is proposed, on the basis of which, a novel RKM clustering algorithm has been developed that specifically gives due consideration to imbalanced clusters. The effectiveness and superiority of this algorithm are demonstrated through simulation and experimental analysis.

Abstract To accommodate the proliferation of heterogeneous network models and protocols, the use of semantic technologies to enable an abstract treatment of networks is proposed. Network adapters are employed to lift network specific data into a semantic representation. Semantic reasoning integrates the disparate network models and protocols into a common data model by making intelligent inferences from low‐level network and device details. Automatic discovery of new devices, monitoring of device state, and invocation of device actions in a generic fashion that is agnostic of network types is enabled. A prototype system called SNoMAC is described that employs the proposed approach operating over UPnP, TR‐069, and heterogeneous sensors. These sensors are integrated by means of a sensor middleware named SIXTH that augments the capabilities of SNoMAC to allow for intelligent management and configuration of a wide variety of sensor devices. A major benefit of this approach is that the addition of new models, protocols, or sensor types merely involves the development of a new network adapter based on an ontology. Additionally, the semantic representation of the network and associated data allows for a variety of client interfaces to facilitate human input to the management and monitoring of the system.

Wireless sensor network (WSN) systems are typically composed of thousands of sensors that are powered by limited energy resources. To extend the networks longevity, clustering techniques have been introduced to enhance energy efficiency. This paper presents a survey on clustering over the last two decades. Existing protocols are analyzed from a quality of service (QoS) perspective including three common objectives, those of energy efficiency, reliable communication and latency awareness. This review reveals that QoS aware clustering demands more attention. Furthermore, there is a need to clarify how to improve quality of user experience (QoE) through clustering. Understanding the users’ requirements is critical in intelligent systems for the purpose of enabling the ability of supporting diverse scenarios. User awareness or user oriented design is one remaining challenging problem in clustering. In additional, this paper discusses the potential challenges of implementing clustering schemes to Internet of Things (IoT) systems in 5G networks. We indicate that clustering techniques enhanced with smart network selection solutions could highly benefit the QoS and QoE in IoT. As the current studies for WSNs are conducted either in homogeneous or low level heterogeneous networks, they are not ideal or even not able to function in highly dynamic IoT systems with a large range of user scenarios. Moreover, when 5G is finally realized, the problem will become more complex than that in traditional simplified WSNs. Several challenges related to applying clustering techniques to IoT in 5G environment are presented and discussed.

This paper presents an observational study conducted at the public environment investigating children's engagement with a social robot within a pretend play scenario. A pretend play system was designed in order to address the challenges of evaluating child-robot interaction by exploiting the advantages of ubiquitous robotics and ambient intelligence of multimodal, multi-sensory perception. The system performed successfully at a children's play centre where a humanoid NAO robot was able to dynamically adapt its gender by changing its gendered voice to match the gender of the child. By analyzing the free play of children, the results obtained confirm the hypothesis of gender segregation within child-robot interaction. These findings are important and ought to be considered when designing robotic applications for children in order to increase robot's perceived likeability, acceptance and engagement.

This paper advocates the need for collaborative sensing which demands that individual sensors collaborate and operate as a collective. To achieve this vision it is necessary for networks to be imbued with distributed intelligence and the capacity to both reason and opportunistically collaborate. The pushing of such computational demands into a computationally challenged network is contrary to considered wisdom. This paper contests that benefits can accrue from such in network collaborative intelligence.

This paper outlines a framework for an info-symbiotic modelling system using cyber-physical sensors to assist in decision-making. Using a dynamic data-driven simulation approach, this system can help with the identification of target areas and resource allocation in emergency situations. Using different natural disasters as exemplars, we will show how cyber-physical sensors can enhance ground level intelligence and aid in the creation of dynamic models to capture the state of human casualties. Using a virtual command & control centre communicating with sensors in the field, up-to-date information of the ground realities can be incorporated in a dynamic feedback loop. Using other information (e.g. Weather models) a complex and rich model can be created. The framework adaptively manages the heterogeneous collection of data resources and uses agent-based models to create what-if scenarios in order to determine the best course of action.

With the rapid development of microgrids, generator excitation control for multi-machine systems to improve the stability of power systems has become a key technical problem. This paper presents an excitation controller design for a typical two-machine system. According to the characteristics of strong nonlinearity, load disturbance and time-varying uncertainty, conventional PID control schemes cannot meet the high quality requirement of excitation control for two- machine systems. A Resource Allocation Network (RAN) based neural PID adaptive generator excitation control is proposed for two-machine systems. The parameters of the PID controller can be adjusted dynamically according to the RAN-enabled online model. The validity of the proposed control strategy is demonstrated by the simulation results.

In many societies, the prevalence of dementia is increasing, posing many challenges, not least for carers. Wandering is a common symptom of dementia, potentially compromising the safety of the person with dementia and constituting a traumatic experience for carers. Thus, there is a need for services that help carers anticipate and manage the inherent risk of wandering while maintaining an appropriate equilibrium between the rights of carers, those with dementia, and indeed society, from a privacy, safety, and duty of care perspective. This paper reports on a usability analysis of one such system that seeks to aid carers manage and alleviate the risk implicit in wandering episodes.

Infrastructure as a Service (IaaS) is a pay-as-you go based cloud provision model which on demand outsources the physical servers, guest virtual machine (VM) instances, storage resources, and networking connections. This article reports the design and development of our proposed innovative symbiotic simulation based system to support the automated management of IaaS-based distributed virtualized data enter. To make the ideas work in practice, we have implemented an Open Stack based open source cloud computing platform. A smart benchmarking application "Cloud Rapid Experimentation and Analysis Tool (aka CBTool)" is utilized to mark the resource allocation potential of our test cloud system. The real-time benchmarking metrics of cloud are fed to a distributed multi-agent based intelligence middleware layer. To optimally control the dynamic operation of prototype data enter, we predefine some custom policies for VM provisioning and application performance profiling within a versatile cloud modeling and simulation toolkit "CloudSim". Both tools for our prototypes' implementation can scale up to thousands of VMs, therefore, our devised mechanism is highly scalable and flexibly be interpolated at large-scale level. Autonomic characteristics of agents aid in streamlining symbiosis among the simulation system and IaaS cloud in a closed feedback control loop. The practical worth and applicability of the multiagent-based technology lies in the fact that this technique is inherently scalable hence can efficiently be implemented within the complex cloud computing environment. To demonstrate the efficacy of our approach, we have deployed an intelligible lightweight representative scenario in the context of monitoring and provisioning virtual machines within the test-bed. Experimental results indicate notable improvement in the resource provision profile of virtualized data enter on incorporating our proposed strategy.

This paper aims to introduce some key parameters for the tracking application in wireless sensor networks.In this paper, the Nearest Closer protocol has been implemented in Jsim simulation platform, and consequently some useful trade-off analysis results among the density, reliability and lifetime have been obtained.Based on these results, two evaluation models are proposed.

In order to inform the design of robotic applications for children, in this paper we describe and report the results of an experiment we conducted in a primary school. Our work investigates the effects of the robot's perceived gender and age on levels of engagement and acceptance of the robot by children across different age and gender groups. Our results show that children across ages relate differently toward perceived robot's age and gender.

Recent trend to steer Wireless Sensor Networks towards a broad outdoor environment allows to monitor harsh areas and prevent a disaster situation. To guarantee an extensive work of the system and smooth cooperation among involved devices a new clustering approach needs to be found. In this paper, we present a new algorithm for an Event-oriented Focal Weight-based clustering for Wireless Sensor Network which will be able to monitor various parameters (physical, biological, and chemical) of the outdoor environment and signal the current situation further to the network. Shown results demonstrate the applicability of the algorithm for the large amount of nodes with various power levels. The use of a dual-band transceiver and advanced processing elements allow to accommodate new features in the nodes and guarantee a high performance with the low power consumption which is crucial for the outdoor system with no human attendance.

Service robots employed in public spaces need to be equipped with specific sensing, reasoning and human-robot interaction capabilities to adapt their interaction style and thus effectively engage with a variety of users. In this paper we present a method used by an ubiquitous robotic system to gather 3D body metrics and use them to robustly estimate age and gender of previously unseen participants in real-world multi-party situations. We evaluate system's performance on 428 children volunteers and compare them with those obtainable with a state of the art software based on face analysis. This work demonstrates that even small number of biometrics can achieve good age and gender estimation results in perceptually challenging environments. Finally, this paper illustrates how the system is used to inform the online adaptation of the behavior of a humanoid robot.

Reducing energy consumption within buildings has been an active area of research in the past decade; more recently, there has been an increased influx of activity, motivated by a variety of issues including legislative, tax-related, as well as an increased awareness of energy-related issues. Energy usage both in commercial and residential buildings represents a significant portion of overall energy consumption; however, much of this may be categorized as waste, that is, energy usage that does not fulfil a definite purpose. In the past decade, the viability of Wireless Sensor Network (WSN) technologies has been demonstrated, leading to increased possibilities for novel services for building energy management. This development has resulted in numerous approaches being proposed for harnessing WSNs for energy management and conservation. This article surveys the state-of-the-art in building energy management systems. A generic architecture is proposed after which a detailed taxonomy of existing documented systems is presented. Gaps in the literature are highlighted and directions for future research identified.

Many routing protocols have advanced the Wire- less Sensor Network (WSN) paradigm with each new method offering unique ways to maximise Quality of Service (QoS) while minimising energy cost to the network. Tree routing is a well examined method with a proven record in offering a high level of service with trees constructed to fulfil a particular routing objective defined by a chosen metric. The tree structure can be maintained with low overall network overhead but exhibits a weakness with regard to load balancing. Without effective load balancing particular nodes in the network may be subject to excessive network load, leading to uneven energy consumption in the network. This in turn can lead to an unwanted scenario of premature node failure. Neighbourhood metrics is presented in this paper as a means to preserve network objectives while achieving improved load distribution in a tree type routing structure. Neighbourhood metrics offer a framework for expand- ing on currently used metrics to include information on the quality of a nodes neighbourhood in addition to the current forwarding route. Neighbourhood metrics is compared to the current state of the art in the form of the Routing Protocol for Low Power and Lossy Networks (RPL) implementation using the direct Expected Transmissions (ETX) metric. Neighbourhood metrics exhibits improved load distribution in a number of open public testbeds.

We increasingly live in a world where sensors have become truly ubiquitous in nature. Many of these sensors are an integral part of devices such as smartphones, which contain sufficient sensors to allow for their use as Augmented Reality (AR) devices. This AR experience is limited by the precision and functionality of an individual device's sensors and the its capacity to process the sensor data into a useable form. This paper discuss the current work on a mobile version of the SIXTH middleware which allows for creation of Sensor Web enabled AR applications. This paper discusses current work on mobile SIXTH, which involves the creation of a sensor web between different Android and non-Android devices. This has led to several small demonstrators which are discussed in this work in progress paper. Future work on the project will be outline the aims of the project to allow for the integration of additional devices so as to explore new abilities such as leveraging additional proprieties of those devices.

With the increasing availability of sensors within smartphones and within the world at large, a question arises about how this sensor data can be leveraged by Augmented Reality (AR) devices. AR devices have traditionally been limited by the capability of a given device's unique set of sensors. Connecting sensors from multiple devices using a Sensor Web could address this problem. Through leveraging this SensorWeb existing AR environments could be improved and new scenarios made possible, with devices that previously could not have being used as part of an AR environment. This paper proposes the use of SIXTH: a middleware designed to generate a Sensor Web, which allows a device to leverage heterogeneous external sensors within its environment to help facilitate the creation of richer AR experiences. This paper will present a worst case scenario, in which the device chosen will be a see-through, Android-based Head Mounted Display that has no access to sensors. This device is transformed into an AR device through the creation of a Sensor Web allowing it to sense its environment facilitated through the use of SIXTH.

It is a common belief that reducing transmission power on a sensor can reduce the energy cost on transmission and therefore reduce the overall power consumption in Wireless Sensor Networks (WSNs). This belief has motivated many studies on transmission power control (TPC). An examination of this belief is presented in this paper, questioning the conditions where TPC can benefit a network. On a single link, we discover there is a limited saving achievable on a sensor itself from reducing transmission power, contradicting previous research. In a star network, however, reducing transmission power can decrease the total energy consumption on other sensors. We conclude that TPC can save energy in a certain network not through reducing a sensor it self's cost as the common belief leads us to expect, but through reducing other sensors' physical and MAC layer receiving.

Networked robotic applications enable robots to operate in distant, hazardous, or otherwise inaccessible environments, such as search and rescue, surveillance, and exploration applications.

One of the most important factors to be considered when developing an application for a wireless sensor network (WSN) is its power consumption. Intelligent power management (IPM) for a WSN is crucial in maximizing the operational longevity. An established regime for achieving this is through the opportunistic hibernation of redundant nodes. Redundancy, however, has various definitions within the field of WSNs and indeed multiple protocols, each operating using a different definition, coexist on the same node. In this paper, we advocate the use of an MAS as an appropriate mechanism by which different stake-holders, each desiring to hibernate a node in order to conserve power, can collaborate. The problem of node hibernation for the heterogeneous sensing coverage areas is introduced and the manner by which it can be solved using ADOPT, an algorithm for distributed constraint optimization, is described. We illustrate that the node hibernation strategy discussed here is more useful than the traditional stack-based approach and motivate our discussion using IPM as an exemplar.

Appliance load monitoring systems aim to achieve per appliance energy decomposition. Such systems however lack automated set-up, which precludes widespread roll-outs. This work illustrates how human supervision can be reduced to a strict minimum; appliance activity states are captured with cheap wireless sensors, which enables accurate automated energy data annotation. This paper demonstrates the accuracy of this technique via an intuitive graphical user interface.

Given the potential scale on which a Wireless Sensor Network (WSN) can be deployed, multi-hop communication will be a pivotal component of the system. When redundant nodes are deployed, which are hibernated opportunistically to conserve energy, it is crucial that sub-graphs of the network are not disconnected by the hibernation of a node. In order to ensure connectivity is preserved for all nodes in the area, a protocol is required that oversees routing integrity is maintained. Since this routing topology must be present at all times, this set represents the minimum number of nodes that must be active at any given time for a functioning network. In this paper, the performance of such a protocol is evaluated, in terms of message delivery and node lifetime for various timing and radius parameters. The selection of these, control the potential energy conserved by the nodes through the hibernation process, which can increase operational longevity. In addition, the radius governs the networks resilience to expired nodes, while timing parameters manage the responsiveness of the topology to failed and exhausted sensors. In this paper, we demonstrate the trade-offs that exist when selecting specific values for these variables as well as the impact these have on a number of Quality of Service (QoS) metrics for WSN performance, namely longevity and message delivery.

Addressing the obesity epidemic that plagues many societies remains an outstanding public health issue. One innovative approach to addressing this problem is Exergaming. A combination of "Exercise" and "Gaming", the objective is to motivate people participate in exercise regimes, usually in their home environment. In this paper a more holistic interpretation of this exercise paradigm is proposed. Freegaming augments Exergaming in a number of key dimensions but especially through the promotion of games in outdoor mobile contexts and within a social environment. The design and implementation of a platform for Freegaming is described and illustrated through the description of a sample game.

Sensing, monitoring and actuating systems are expected to play a key role in reducing buildings overall energy consumption. Leveraging sensor systems to support energy efficiency in buildings poses novel research challenges in monitoring space usage, controlling devices, interfacing with smart energy meters and communicating with the energy grid. In the attempt of reducing electricity consumption in buildings, identifying individual sources of energy consumption is key to generate energy awareness and improve efficiency of available energy resources usage. Previous work studied several non-intrusive load monitoring techniques to classify appliances; however, the literature lacks of an comprehensive system that can be easily installed in existing buildings to empower users profiling, benchmarking and recognizing loads in real-time. This has been a major reason holding back the practice adoption of load monitoring techniques. In this paper we present RECAP: RECognition of electrical Appliances and Profiling in real-time. RECAP uses a single wireless energy monitoring sensor easily clipped to the main electrical unit. The energy monitoring unit transmits energy data wirelessly to a local machine for data processing and storage. The RECAP system consists of three parts: (1) Guiding the user for profiling electrical appliances within premises and generating a database of unique appliance signatures; (2) Using those signatures to train an artificial neural network that is then employed to recognize appliance activities (3) Providing a Load descriptor to allow peer appliance benchmarking. RECAP addresses the need of an integrated and intuitive tool to empower building owners with energy awareness. Enabling real-time appliance recognition is a stepping-stone towards reducing energy consumption and allowing a number of major applications including load-shifting techniques, energy expenditure breakdown per appliance, detection of power hungry and faulty appliances, and recognition of occupant activity. This paper describes the system design and performance evaluation in domestic environment.

Recent advances in low-power wireless networking have enabled remote and nonintrusive access to households' electric meter readings, allowing direct real-time feedback on electricity consumption to home owners and energy providers. Fine-grained electricity billing based on appliance power load monitoring has been investigated for more than two decades, but has not yet witnessed wide commercial acceptance. In this paper, we argue that the required human supervision for profiling and calibrating appliance load monitoring systems is a key reason preventing large-scale commercial roll-outs. We propose ANNOT, a system to automate electricity data annotation leveraging cheap wireless sensor nodes. Characteristic sensory stimuli captured by sensor nodes placed next to appliances are translated into appliance operating state and correlated to the electricity data, autonomously generating the annotation of electricity data with appliance activity. The system is able to facilitate the acquisition of appliance signatures, training data and validate the monitoring output. We validate the concept by integrating the automated annotation system to the RECAP appliance load monitoring system.

Appliance load monitoring systems are designed to disaggregate the power load of a building in order to estimate the nature of individual loads, providing a real-time fine-grained recognition of active appliances. Monitoring nonintrusively appliances' contributions to a given load enables a wide range of applications, ranging from electricity bill decomposition to accurate electricity user profiling. This work demonstrates a real implementation of such appliance load monitoring system. An intuitive graphical user interface is proposed to drive the system setup for profiling appliances' signatures and for visualising the monitoring output.

Energy efficiency is a central challenge in sensor networks, and the radio is a major contributor to overall energy node consumption. Current energy-efficient MAC protocols for sensor networks use a fixed low-power radio mode for putting the radio to sleep. Fixed low-power modes involve an inherent trade-off: deep sleep modes have low current draw and high energy cost and latency for switching the radio to active mode, while light sleep modes have quick and inexpensive switching to active mode with a higher current draw. This paper proposes adaptive radio low-power sleep modes based on current traffic conditions in the network. It first introduces a comprehensive node energy model, which includes energy components for radio switching, transmission, reception, listening, and sleeping, as well as the often disregarded microcontroller energy component for determining the optimal sleep mode and MAC protocol to use for given traffic scenarios. The model is then used for evaluating the energy-related performance of our recently proposed RFID impulse protocol enhanced with adaptive low-power modes, and comparing it against BMAC and IEEE 802.15.4, for both MicaZ and TelosB platforms under varying data rates. The comparative analysis confirms that RFID impulse with adaptive low-power modes provides up to 20 times lower energy consumption than IEEE 802.15.4 in low traffic scenario. The evaluation also yields the optimal settings of low-power modes on the basis of data rates for each node platform, and provides guidelines and a simple algorithm for the selection of appropriate MAC protocol, low-power mode, and node platform for a given set of traffic requirements of a sensor network application.

The efficient management of scarce network resources, including energy and bandwidth, represents a central challenge for wireless sensor networks. The current trend in resource management relies on the introduction of control mechanisms, such as control message exchanges, node-specific addressing, and storage of partial network state information. These mechanisms typically incur communication and processing overhead that does not scale well for larger or denser networks. Instead of introducing control mechanisms for network resource management, this article proposes and evaluates a Directed Broadcast with Overhearing (DBO) approach for sensor networks that combines directed broadcast at the network layer with CSMA and packet overhearing at the MAC layer. Through avoidance of control messaging and exchange of network state information, DBO trades off limited packet duplication overhead for control messaging overhead. This article introduces an analytical model that provides the basis for DBO evaluation and for analysis of the approach's transient packet retransmissions, route convergence, and energy consumption in the average and worst cases. We also present the model implementation details and the simulation experiments that explore the suitability of DBO for networks of different sizes with three different radio models that vary the width of grey regions, and we compare DBO's energy consumption against conventional unicast beacon-based and snooping-based routing protocols. The results indicate that that DBO's route convergence requires an average of five hops for ideal radio reception, seven hops for narrow grey regions, and twelve hops for wide grey regions. These results confirm that DBO shifts energy consumption from critical nodes near the base station to nodes near the source. The overall energy consumption of limited packet duplication overhead with DBO compared to unicast routing shrinks for medium- to large-size networks, rendering it more favorable than conventional communication approaches for large and dense sensor networks.

Energy efficiency is a central challenge in battery- operated sensor networks. Current energy-efficient mechanisms employ either duty cycling, which reduces idle listening but does not eliminate it, or low power wake-up radio, which adds complexity and cost to the sensor platform. In this paper, we propose a novel mechanism called RFIDImpulse that uses RFID technology as an out-of-band wake-up channel for sensor networks. RFIDImpulse is an on-demand mechanism that enables nodes to sleep until they have to send or receive packets. It relies on IEEE 802.15.4 radio to emulate an RFID reader at a sender node, and on an off-the-shelf RFID tag attached to the external interrupt pin of each sensor node. The sender can simply activate the receiver's tag before sending it data packets. This setup enables both radio and microcontroller to go into deep sleep mode until they need to be active. We develop an analytical model to evaluate the energy tradeoffs of RFIDImpulse, and then evaluate the mechanism against BMAC and IEEE 802.15.4 in high and low traffic scenarios. The results confirm that RFIDImpulse reduces the energy consumption relative to both protocols for low and medium traffic scenarios, and they reveal the thresholds for adaptive activation of RFIDImpulse based on traffic load.

Power management for WSNs can take many forms, from adaptively tuning the power consumption of some of the components of a node to hibernating it completely. In the later case, the competence of the WSN must not be compromised. In general, the competence of a WSN is its ability to perform its function in an accurate and timely fashion. These two, related, Quality of Service (QoS) metrics are primarily affected by the density and latency of data from the environment, respectively. Without adequate density, interesting events may not be adequately observed or missed completely by the application, while stale data could result in event detection occurring too late. In opposition to this is the fact that the energy consumed by the network is related to the number of active nodes in the deployment. Therefore, given that the nodes have finite power resources, a trade-off exists between the longevity and QoS provided by the network and it is crucial that both aspects are considered when evaluating a power management protocol. In this paper, we present an evaluation of a novel node hibernation technique based on interpolated sensor readings according to these four metrics: energy consumption, density, message latency and the accuracy of an application utilising the data from the WSN. A comparison with a standard WSN that does not engage in power management is also presented, in order to show the overhead in the protocols operation.

As the cost and size of WSN nodes reduce, it will be possible for deployments to provide node densities far in excess of the resolution requirements of any one single application. When this is the case, multiple, independent Virtual Sensor Networks (VSNs) can be maintained on the same physical deployment that satisfy distinct application requirements. In addition to this, nodes not part of any VSN can be considered redundant and can therefore be opportunistically hibernated. In this paper, the disadvantages of a layered approach to achieving such a system are demonstrated. An architecture based on a Multi-Agent System (MAS) is proposed as an alternative to alleviate some of the problems with the layered technique. Utilising a MAS facilitates the distribution of new VSNs onto a deployed WSN and permits heterogeneous timing regimes to be implemented that allow optimal performance of the individual VSNs.

Intelligent agents offer a viable paradigm for enabling AmI applications and services. As WSN technologies are anticipated to provide an indispensable component in many application domains, the need for enabling the agent paradigm to encompass such technologies becomes more urgent. The resource-constrained ad-hoc nature of WSNs poses significant challenges to conventional agent frameworks. In particular, the implications for agent functionality and behaviour in a WSN context demand that issues such as unreliable message delivery and limited power resources, amongst others, be considered. In this paper, the practical issues of agent migration and communication are considered in light of WSN constraints. The discussion is illustrated through a description of approaches adopted by Agent Factory Micro Edition (AFME).

Abstract—Energy-efficient performance is a central challenge in sensor network deployments, and the radio is a major contributor to overall energy node consumption. Current energy-efficient MAC protocols for sensor networks use a fixed low power radio mode for putting the radio to sleep. Fixed low power modes involve an inherent tradeoff: deep sleep modes have low current draw and high energy cost and latency for switching the radio to active mode, while light sleep modes have quick and inexpensive switching to active mode with a higher current draw. This paper proposes adaptive radio low power sleep modes based on current traffic conditions in the network, as an enhancement to our recent RFIDImpulse low power wake-up mechanism. The paper also introduces a comprehensive node energy model, that includes energy components for radio switching, transmission, reception, listening, and sleeping, as well as the often disregarded micro-controller energy component to evaluate energy performance for both MicaZ and TelosB platforms, which use different MCU’s. We then use the model for comparing the energy-related performance of RFIDImpulse enhanced with adaptive low power modes with BMAC and IEEE 802.15.4 for the two node platforms under varying data rates. The comparative analysis confirms that RFIDImpulse with adaptive low power modes provides up to 20 times lower energy consumption than IEEE 802.15.4 in low traffic scenario. The evaluation also yields the optimal settings of low power modes on the basis of data rates for each node platform, and it provides guidelines for the selection of appropriate MAC protocol, low power mode, and node platform for a given set of traffic requirements of a sensor network application. I.

Symbol synchronization in traditional hardware- driven communication systems has relied on the transmission of training sequences of symbols just before the beginning of the frame symbols. The use of training sequences is not suitable for software-driven communication systems, such as lightweight acoustic underwater sensor networks [4,5], in which the high symbol loss rate may cause the loss of training symbols, preventing accurate symbol synchronization. Software-driven communication networks require symbol synchronization that is resilient to a high loss environment, that does not represent large communication or processing overhead, and that is tunable to the noise profile of different environments. These requirements are emphasized for mote-based acoustic underwater sensor networks in which the bandwidth and processing capability are sparse. This paper proposes the use of a short signature synchronization symbol (S4) as both a preamble and post-amble to enable receiver synchronization in mote-based acoustic communication systems that rely on software modems. To synchronize to an incoming signal, the receiver performs cross-correlation of N reference signature symbols with the incoming signal to identify the beginning of the preamble and post-amble. The output of the cross-correlation yields 2N peak values, from which the receiver chooses the sharpest and most symmetric for synchronization to the beginning of the frame. Empirical experiments confirm a synchronization accuracy within 5 ms in air within a range of 10.5 m, and 11 ms in water within a range of 15 m.

Endowing robots with a social interface is often costly and difficult. Virtual characters on the other hand are comparatively cheap and well equipped but suffer from other difficulties, most notably their inability to interact with the physical world. This paper details our wearable solution to combining physical robots and virtual characters into a mixed reality agent (MiRA) through mixed reality visualisation. It describes a pilot study demonstrating our system, and showing how such a technique can offer a viable alternative cost effective approach to enabling a rich social interface for human-robot interaction.

The emerging technology of Wireless Sensor Networks offers the potential for a new and unparalleled level of surveillance for events occurring in an environment. The quality of surveillance will be directly related to the quantity and density of sensors that can be deployed, and that are actively participating in the monitoring at any given instant. In opposition to this is the requirement for judicious power management of the network in order to prolong its operational lifespan by minimizing node activity. In this paper we explore the effect on surveillance quality when using interpolation error as a mechanism for deciding node activations. More specifically we examine the effect of using this signal based hibernation technique on target localization error and node activity.

This paper contests that Mixed Reality (MR) offers a potential solution in achieving transferability between Human Computer Interaction (HCI) and Human Robot Interaction (HRI). Virtual characters (possibly of a robotic genre) can offer highly expressive interfaces that are as convincing as a human, are comparably cheap and can be easily adapted and personalized. We introduce the notion of a mixed reality agent, i.e. an agent consisting of a physical robotic body and a virtual avatar displayed upon it. We realized an augmented reality interface with a Head-Mounted Display (HMD) in order to interact with such systems and conducted a pilot study to demonstrate the usefulness of mixed reality agents in human-robot collaborative tasks.

Notoriously, energy-efficient MAC protocols cause high latency of packets. Such delays may well increase when a routing protocol is applied. Therefore, quantifying the end-to-end delay and energy consumption when low duty cycle MAC and routing protocols are jointly used, is of particular interest. In this paper, we present a comprehensive evaluation of the MERLIN (MAC and efficient routing integrated with support for localization) protocol. MERLIN integrates MAC and routing features into a single architecture. In contrast to many sensor network protocols, it employs a multicast upstream and multicast downstream approach to relaying packets to and from the gateway. Simultaneous reception and transmission errors are notified by using asynchronous burst ACK and negative burst ACK. A division of the network into timezones, together with an appropriate scheduling policy, enables the routing of packets to the closest gateway. An evaluation of MERLIN has been conducted through simulation, against both the SMAC and the ESR routing protocols,which is an improved version of the DSR algorithm. The results illustrate how both SMAC and ESR, jointly used in low duty cycle scenarios, can cause an impractical and very high end-to-end delays. MERLIN, as an integrated approach, notably reduces the latency, resulting in nodes that can operate in a very low duty cycle. Consequently, an extension of the operative lifetime of the sensor network is achieved.

The 9th International Conference on Speech Communication and Technology (Interspeech 2005), Lisbon, Portugal, September, 2005

How mobile computing applications and services may be best designed, implemented and deployed remains the subject of much research. One alternative approach to developing software for mobile users that is receiving increasing attention from the research community is that of one based on intelligent agents. Recent advances in mobile computing technology have made such an approach feasible. We present an overview of the design and implementation of an archetypical mobile computing application, namely that of an electronic tourist guide. This guide is unique in that it comprises a suite of intelligent agents that conform to the strong intentional stance. However, the focus of this paper is primarily concerned with the results of detailed user evaluations conducted on this system. Within the literature, comprehensive evaluations of mobile context-sensitive systems are sparse and therefore, this paper seeks, in part, to address this deficiency.

Providing an intuitive and ubiquitous interface to those services aimed at the mobile computing community continues to pre-occupy both service providers and certain sections of the research community. In this short paper, we present a snapshot of a system that is under ongoing development and reflect briefly on the initial results of some user evaluations. Based on these, we identify some critical problems with the current implementation and present a design orientated towards significantly improving the end user experience and making the interface more adaptable such that it meets the expectations and requirements of the user.

The potential of a mobile personalised automated shopping protocol holds allure for end-user and commercial protagonist alike. The shopper is alleviated from the shopping task, in the process redressing a prevalent ailment of modern life- the 'cash rich, time poor imbalance'. This paper reports on recent efforts in autonomous mobile agent mediated retailing and considers how best to catalyse the development of a ubiquitous and semiautonomous macro economy. We develop upon previous work with our functional system, Easishop, a FIPA-compliant mobile agent based cross merchant product comparison shopping infrastructure.

Agent design has to date concerned itself with the issues pertaining to a single body embedded in a single environment, whether virtual or real. This paper discusses the notion of an agent capable of migrating between information spaces (physical worlds, virtual reality, and digital information spaces). An architecture is presented that facilitates agent migration and mutation within such environments. This will in turn support agent evolution the ultimate in agent adaptivity.

The attractions of a stable, integrated, real world mobile automated shopping system are clear. For the shopper, the system acquires, on behalf of the user, a sought product without the user entering the retail outlet. For the retailer, transactions could occur cheaply, quickly and with minimum drain on the resources of the retailer. We describe an agent-based, location-aware, automated ubiquitous commerce (uCommerce) system. Within this paper we introduce a set of interoperating technologies to enable an automated uCommerce system using mobile agent interaction facilitated by the Bluetooth wireless radio transmission medium. We describe an architecture and a prototype - EasiShop - based on these ideas and illustrate the operation of the prototype in an example scenario.

This paper explores the general landscape of virtual communities and reflects upon their relevance to their agent community counterparts. It examines several disparate virtual communities in order to identify common threads and characteristics, which underpin social cohesion. Herein we explore how concepts such as awareness, social presence and embodied state constitute key parameters in the derivation of social models of trust, reliance and dependence. In turn, social realism is introduced, and the extent to which this can form the basis of a social scaffolding for the support of effective agent communities is reflected upon. The concept of agent chameleons is characterized together with how the capability of metamorphosis influences (positively and negatively) agent community interaction and the construction of models of trust, respect and regard is considered. Such evolution and the ability of the agent to invoke this process are, we contest, the ultimate manifestations of autonomy.

The paper explores the evolution of the concept of community in the light of computer mediated immersive virtual environments. The traditional concept of community has become strained in its attempts to capture the evolving virtual community. We believe the concept of the virtual community is of paramount importance and examine the extent to which this is being redefined to cater for it. We examine the management and mediation of such an environment and specifically the social process associated with the cohabited users. We advocate the use of multi-agent systems in delivering this functionality.