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Internet of Things (IoT) is usually associated with the acquisition of sensor node information and controlling of things. However, the inadequateness of location information of these sensor nodes compromises the intelligence of the IoT network. With an emphasis on indoor environments, localisation in IoT has become a very important area for research due to the fact that well-established positioning services such as the Global Positioning System (GPS) are not viable indoors. In essence, the difficulty for satellite signals of such existing systems to reach indoor environments causes huge localisation inaccuracies. Therefore, indoor localisation in recent time has been based on Wireless Sensor Networks (WSN). In this work, distributive indoor localisation accuracy and computational performance as implemented in real life towards IoT are of concern. Therefore, this work presents a methodology comprised of a mix of existing techniques to integrate indoor localisation of sensor nodes and IoT in a realistic environment and at an acceptable degree of localisation accuracy. The major contribution presented in this work is the attainment of an acceptable level of localisation accuracy while maintaining high computational efficiency on a developed hardware sensor node prototype. In achieving this, the implementation issues regarding the complexity of localisation algorithm and hardware computational capabilities are tackled. In ensuring that this work is aligned with the IoT context, an Internet and Intranet enabled connectivity for real-time access to the location information of sensor nodes is developed and presented. This work is also achieved through the use of a distributive online based localisation algorithm based on Kalman filtering Received Signal Strength Indicator (RSSI) and Gauss–Newton Algorithm (GNA). A sensor node prototype capable of handling complex computations is developed and presented. A gateway device and an IoT framework are also proposed and implemented based on Linux, Apache, MySQL, PHP (LAMP) server to provide global and local access to real-time location information of the sensor nodes. The algorithm is first simulated for pre-validation and then ported to the sensor node prototypes. The computational power of the hardware is analysed based on the time it takes to perform the GNA based localisation process towards convergence. The Root Mean Square Error (RMSE) is computed for analysing the accuracy level of the proposed concept. This work further presents findings based on the effect of node orientation changes as the localisation process is performed and logged, which is an issue of great concerned to be tackled in future works.

The Narrowband Internet of Things (NB-IoT) has been introduced in the 3rd Generation Partnership Project (3GPP) Rel-13 with the aim to provide low-cost, low-power, wide-area cellular connectivity for the Internet of Things. With the exponentially increasing number of connected wireless devices in the order of 100 billion, it has become crucial that researchers develop efficient resource management techniques to meet the 5th Generation (5G) quality of service (QoS) requirements. Recently, several research challenges including the low modulation data rates, energy-expensive channel coding techniques, and the fast-growing number of connected devices have been identified as some of the main issues encountered in the design and deployment of NB-IoT systems. In addition, several techniques have emerged in the literature to resolve some of these challenges of NB-IoT systems. However, the research activities towards the enhancement of the NB-IoT resource management are yet to continue in the next half-decade before, high data rates, energy-efficient, and scalable NB-IoT specifications can be released for the standardisation and commercialisation. Considering the limited number of existing surveys of such technical enhancement approaches in a broader perspective (i.e. energy efficiency, data rate performance and scalability) and also the non-existence, to the best of our knowledge, of a comparative survey, this paper seeks to elaborate, describe and compare the performances of such resources management approaches. Of the multiple NB-IoT resources, the focus of this paper is on the data rate, energy efficiency, and scalability enhancement schemes that have been proposed for the last three years. The contribution of the paper lies in the analysis, synthesis, comparison and summarised alignments of some of the major existing schemes towards identifying challenges faced by the NB-IoT development. Finally, this work seeks to identify research challenges, open questions, and opportunities that could arise from the existing techniques analysed.

The Media Access Control (MAC) serves as an imperative part of wireless communication networks which enables efficient provisioning of communication resources for device interconnectivity and ensures Quality of Service (QoS). The emergence of Large-Scale Internet of Things (LS-IoT) networks is characterised by a multi-domain distributed wireless communication network that provides end-to-end connectivity for a multitude of active heterogeneous Machine-to-Machine (M2M) devices. The nature of LS-IoT networks requires robust and scalable MAC protocols to manage concurrent, dynamic and massive media access and resource allocation. Several reviews have been conducted on MAC protocols with a handful of them focused on LS-IoT networks or massive M2M networks. In this paper, the characterisation of LS-IoT networks and the MAC component are extensively discussed delineating the impact of LS-IoT on the MAC. Emerging research issues on the MAC for LS-IoT networks including high collision probability, high control overheads, spectrum constraints, timing constraints, hardware constraints, energy consumption and hidden terminals are discussed. Some recently proposed solutions in literature for enhancing the MAC in LS-IoT networks are discussed under LS-IoT specific MAC protocol enhancement classifications. The contributions and drawbacks of the recent solutions in literature are discussed and summarised. The Future direction that is oriented towards the use of virtualisation within the framework of Network Function Virtualisation (NFV) and Software-Defined Networking (SDN) approaches is proposed. This is aimed at providing a dynamic distributed multidimensional MAC resource scaling approach for LS-IoT access network devices to ensure a robust and effective MAC for massive M2M devices in LS-IoT networks.

The novel coronavirus (COVID-19), declared by the World Health Organization (WHO) as a global pandemic, has brought with it changes to the general way of life. Major sectors of the world industry and economy have been affected and the Internet of Things (IoT) management and framework is no exception in this regard. This article provides an up to date survey on how a global pandemic such as COVID-19 has affected the world of IoT technologies. It looks at the contributions that IoT and associated sensor technologies have made towards virus tracing, tracking and spread mitigation. The associated challenges of deployment of sensor hardware in the face of a rapidly spreading pandemic have been looked into as part of this review article. The effects of a global pandemic on the evolution of IoT architectures and management have also been addressed, leading to the likely outcomes on future IoT implementations. In general, this article provides an insight into the advancement of sensor-based E-health towards the management of global pandemics. It also answers the question of how a global virus pandemic has shaped the future of IoT networks.

Based on the recent studies, by 2020 devices connected to internet are predicted to increase up to 22 billion. In compliance with the rapid growth of internet of things (IoT), Low power wide area Network (LPWA) technologies play a big role in connecting billions of devices. This paper aims to analyze the existing standard NB-IoT, LORA, Wi-Fi HaLow (802.11ah) used as low power wide area Network (LPWA) in wireless network, in term of their power efficiency, QoS, cost and coverage distance. The outcome of the research shows that each technology presents its advantages and disadvantages based on the type of the application considered. It is shown that despite the improvement of researches on existing LPWA technologies, there are still issues that need to be addressed. Thus, this paper gives some open issues for future research.

Visual localization of indoor environments enables an autonomous system to recognize its current location and environment using sensors such as a camera. This paper proposes a method for visual recognition of indoor environments leveraging on existing object detection, ontology, Bayesian-like framework, and speeded-up robust features (SURF) algorithms. Objects detected in such an environment are fed into a Bayesian-like framework for domain recognition. Finally, the SURF localizes the predicted environment. One of the objectives of the proposed model is to eliminate the image-based training phase encountered in traditional place recognition algorithms. The proposed model does not rely on any visual information on the environment for training. Experiments are carried out on two publicly available datasets with promising results.

Small unmanned aerial vehicles applications had erupted in many fields including conservation management. Automatic object detection methods for such aerial imagery were in high demand to facilitate more efficient and economical wildlife management and research. This paper aimed to detect hoofed animals in aerial images taken from a quad-rotor in Southern Africa. Objects captured in this way were small both in absolute pixels and from an object-to-image ratio point of view, which were not perfectly suit for general purposed object detectors. We proposed a method based on the iconic Faster region-based convolutional neural networks (R-CNN) framework with atrous convolution layers in order to retain the spatial resolution of the feature map to detect small objects. A good choice of anchors was of prime importance in detecting small objects. The performance of the proposed Faster R-CNN with atrous convolutional filters in the backbone network was proven to be outstanding in our scenario by comparing to other object detection architectures.

Integral and crucial to performance of wireless sensor networks (WSNs), and specifically industrial wireless sensor network (IWSN) is stable, robust, reliable, and ubiquitous communication...

Nowadays, the growing adoption of the Internet of Things (IoT) is reshaping the telecommunication landscape and has penetrated every aspect of our lives with influential applications on smart health, home automation, smart logistics, smart industries and smart cities. These advanced technologies bring about numerous benefits and has begun to play a major role in daily lives, particularly data mining applied in precision agriculture to discover knowledge. Also in agro-industrial production chain, the combination of wireless and distributed specific sensor devices with the simulation of climatic conditions in order to track the evolution of grapes for wineries is outstanding. Mobile IoT such as narrowband-IoT (NB-IoT) and the long-term evolution (LTE) for machines (LTE-M) are significant innovations of this accelerating development of IoT technologies. In the era of ubiquitous communication where everything is connected to the internet, NB-IoT systems are expected to offer better quality-of-services (QoS) to end users than the traditional IoT paradigm. However, offering better QoS satisfaction to end users will become a great challenge due to the bottleneck caused by the dual problem of increasing IoT use cases and the shortage of wireless spectrum resources. Whilst discussing the recent innovative solutions of NB-IoT resource allocation, significant challenges and open issues related to the real-time implementation of NB-IoT are identified and discussed. Therefore, this paper gives a general overview of the resource allocation solutions in this NB-IoT innovation and further suggests and motivates for the intelligentization of future resource allocation solutions (i.e., the use of artificial intelligence (AI) strategies).

Context Sensorimotor rhythms (SMR) have been the neuronal phenomena of choice in non-invasive EEG-based endogenous brain computer interfaces (BCIs) for more than two decades and SMR-based BCIs have achieved the highest degree of freedom control so far. Nevertheless, they are subject to long periods of training prior to attaining a satisfactory level of control requiring users to learn to modulate their rhythms. The goal of this work is to analyse this problem, discuss the causes of the slow rise in performance and provide recommendations on alternative solutions to quicken control attainment. Methods The study has been conducted by both theoretical and empirical analysis. A theoretical model has been developed that explains the principle operation of SMR-based BCIs focusing on major performance contributors respectively the user, periodic feature selection and the translation model thus contrasting user adaptation and machine learning. Five able-bodied subjects (age: 26 ± 2.55 ) participated in six sessions of online computer cursor control experiments over three weeks to evaluate control attainment performances and gather data for statistical analysis (∼1152 trials per subject). Correlation ( r 2 ) between user control features and target position over sessions was assessed as an estimate of neural adaptation and the predictive power of the translation algorithm (10 × 10 fold cross-validation) was calculated over sessions as an estimate of machine adaptation. Auxiliary performance metrics were evaluated. Results Features-target correlation increased over sessions, while at the same time the predictive accuracy ( R 2 ) of the translation model remained averagely steady and very low ( R b e s t 2 = 0.04 ) demonstrating continuous user adaptation and low model predictive accuracy. Periodic feature selection was theoretically discussed to be very instrumental and its relevance was empirically illustrated. Conclusions The study concludes that the slow control attainment in SMR-based BCIs is due to its reliance on user training (neural adaptation) which is adaptive but too slow in the context of SMR modulations and due to the weak decoding of the neuronal phenomenon utilised by the user. As a recommendation, the optimality of the feature selection algorithm could be looked at to guarantee the use of the most relevant features. However and most importantly the predictive power of the translation model should be significantly improved in order to quicken control attainment as thereafter the control attainment effort could be shifted from neural adaptation to machine learning.

The utilisation of smart antenna (SA) techniques in future wireless and cellular networks is expected to have an impact on the efficient use of the spectrum and the optimization of service quality. This is because SAs can enhance the maximization of output power of the signal in desired directions amongst a whole lot of functions. In spite of these benefits of SAs, long range communications still face unsolved challenges such as signal fading. Therefore, this paper focuses on enhancing the recursive least squares (RLS) in SA design for long range communication networks. The conventional RLS algorithm does not need any matrix inversion computations because the inverse correlation matrix is determined directly. Therefore, the RLS saves computational power. Hence, we have enhanced the RLS algorithm by introducing a constant m to the gain factor in order to yield an improved gain vector. Results obtained from our simulations show that the enhanced RLS reduces mean square error (MSE), smoothens filter output and improves SNR when compared to the conventional methods. These benefits further result in antenna the gain improvement leading to an increased range and directivity of the smart antenna over a long range communication networks.

It is widely known that Remote healthcare monitoring can be a solution to provide an alternative healthcare service that can reduce the amount of strain that the current health care systems experience with the ever increasing demand of health care services world wide. However monitoring a patient remotely requires an accurate interpretation of the data regarding the patients condition. This position paper presents a Fuzzy expert system used to reason medical contexts from a Body Area Network consisting of several sensors monitoring vital healthcare indicators. Fuzzy Logic is used to aggregate the data from the sensors and handle the reasoning required to abstract high-level context information from the low-level context from the sensors and determine the patients condition.

Currently, organizations are growing and continuously improving their services while reducing budget in a competitive market. This is achieved by reusing existing well designed and proved services delivered by third parties in the cloud. Additionally, multi- domain collaborative cloud environments are a promising approach that enables to build and deliver new adaptive, distributed and reliable services. In this case, the data interoperability, the process interoperability and the security open new challenges and issues. In this paper, we propose semantic-based decision making process and consistent with XACML architecture to deal with contextual access control in multi-domain collaborative cloud environments.

Internet of Things (IoTs) is an emerging technology that is envisioned to revolutionise the enabling environment and to provide autonomous, ubiquitous and pervasive computing within Wireless Sensor Networks (WSNs). IoTs are usually associated with the acquisition of sensor node information and controlling of “things”. However, the absence of location information of these sensor nodes compromises the intelligence of the IoT network. Therefore, this work is motivated by the recent advances in the two important areas of WSNs namely, indoor localisation and IoTs. This paper therefore presents a framework that integrates indoor localisation of sensor nodes and IoTs in a real world scenario. The focus is mainly on the implementation issues regarding localisation algorithm complexity, hardware computational capabilities and Internet/Intranet enabled connectivity for access to the sensor nodes’ location information. A sensor node prototype is developed using specialised electronic components and proprietary protocols to provide a capable platform for embedding a distributive online localisation algorithm based on Received Signal Strength (RSSI) and Gauss-Newton Algorithm (GNA). The algorithm is first simulated before porting it to the sensor node prototypes. A gateway device and an IoT framework are also proposed and implemented based on Linux, Apache, MySQL, PHP (LAMP) server to provide global and local access to sensor nodes’ location information. The Root Mean Square Error (RMSE) of the IoT logged estimated coordinates from the prototype nodes and the estimated coordinates from the simulation are computed and compared. The computational power of the hardware is analysed based the time it takes to perform the GNA based localisation process.

A number of MAC scheduling protocols have been proposed in literature and by standardisation groups in order to meet the requirements and specifications of the M2M communication and the Internet of Things (IoT) network. With the fast growth of heterogeneous M2M devices in the IoT network, the MAC protocol design becomes very challenging. New specifications and requirements for IoT and M2M communication are continuously established. This prompted the IEEE WLAN standardisation body to develop the new IEEE 802.11ah standard targeted at accommodating M2M communication into the Internet framework. The standard outlines a number of key enhancements to the Physical (PHY) layer and the Medium Access Control (MAC) layer. The enhancements have been documented and published. The published standard, however, presents ambiguities creating open discussions about the MAC layer. Some of the proposed amendments are not explicit. The standard also provides a large variety of optional enhancements in the MAC layer. This could potentially lead to a number of interoperable issues which can affect the deployment of the standard. This paper focuses on studying the 802.11ah enhancements for scheduling of Stations (STAs) by modelling and evaluating the theoretical limitations of the Traffic Indication Map (TIM) and Restricted Access Window(RAW) features. The paper highlights the potential challenges that may affect the implementation of the protocol from the results of the evaluation presented.

This paper introduces an adaptive integral sliding mode controller for exoskeletons. The controller design is based on the hypothesis that only classical properties are known such as parameters’ bounds, and all other functions are unknown. To ensure the convergence in position, velocity and acceleration for desired trajectories, disturbances are supposed to be bounded. The closed-loop stability of the system in the sense of Lyapunov is demonstrated. The effectiveness of the proposed approach is proved in real time using a 2-DOF upper limb exoskeleton in the rehabilitation context taking into account the security and the safety of the user.

Wheelchairs are broadly accepted and are widespread because of the assistance they provide to people with limited mobility. The design of a good controller generally involves the formulation of a comprehensive wheelchair model. Most dynamic models in the literature presume non-inclined planer surfaces within-doors, and therefore fail to take the combined effects of both gravitational forces and rolling friction on the usable-traction into consideration. Wheel-slip situations are also commonly neglected. This paper contributes to wheelchair modeling by proposing and formulating a dynamic model that considers the effects of rolling friction and gravitational potential on the wheelchair’s road-load force, on both inclined and non-inclined surfaces. The dynamic model is derived through the Euler Lagrange procedure, and wheel slip is determined by an approach that reduces the convectional number of slip-detection encoders. In the closed-loop model, the input-output feedback controller is proposed for tracking the user inputs by torque compensation. The optimality of the resulting minimum-phase closed-loop system is then ensured through the performance index of the non-linear continuous-time generalized predictive control with good simulation results.

Network coding consists of intelligently aggregating data packets by means of binary or linear combinations. Recently, network coding has been proposed as a complementary solution for energy efficient multi-hop routing in Wireless Sensor Networks (WSNs). This is because network coding, through the aggregation of packets, considerably reduces the number of transmissions throughout the network. Although numerous network coding techniques for energy efficient routing have been developed in the literature, not much is known about a single survey article reporting on such energy efficient network coding within multi-hop WSNs. As a result, this paper addresses this gap by first classifying and discussing the recent developed energy efficient network coding techniques. The paper then identifies and explains open research opportunities based on analysis of merits of such techniques. This survey aims at providing the reader with a brief and concise idea on the current state-of-art research on network coding mainly focusing on its applications for energy efficient WSNs.

In the Internet of Things (IoT), web objects and services can be seen as distributed and cooperative agents that need to collaborate in order to reach advanced functionalities and also to optimize the overall quality offered to the end users. For this purpose, we propose in this paper a distributed and optimal QoS services selection approach based on Multi-Agents paradigm and Distributed Constraints Optimization Formalism (DCOP). Hence, we revisited a DCoP technique for developing a new complete algorithm for service selection: the’Synch4QoS’ implemented under a multi-agent architecture using a real-time Web protocol for communi- cation. The proposed algorithm takes into accounts the specificities of the service composition context and the satisfaction of the global user's constraints.

The key idea to spectrum decision in Cognitive Radio Networks (CRN) is the selection of the best available spectrum band to satisfy Secondary Users (SUs) Quality of Service (QoS) requirements, without interfering with transmission of the licensed or Primary Users (PU). This challenging task requires a very good cooperation between users with different demands for the best use of spectrum channels of different characteristics in a heterogeneous network. In this paper we propose a linear optimization spectrum selection algorithm based on the evaluation of different channels characteristics and a linear optimization solution to ensure the selection of the best available spectrum satisfying the demands of the Secondary Users. The simulation results show that this approach ensures both the best transmission throughput and the transmission quality of service.

Computer vision techniques are applied to perform automatic wildlife surveying and animal monitoring. Animal detection in aerial videos is challenging because of the complexity of wild environments. In this paper, a method for moving animal detection is proposed by taking advantage of global patterns of pixel motion. In the video dataset, where animals make obvious movement against the background, motion vectors of each pixel are estimated by applying optical flow methods. A coarse segmentation then removes most parts of the background by applying a pixel velocity threshold. Based on the segmented regions, another threshold was employed to filter out negative candidates that could belong to the background. The pros and cons of this method are discussed.

The next generation 5G radio access network (RAN) system is believed to be a true world wide wireless web (WWWW). This is because such system will seamlessly and ubiquitously connect everything, and support at least 1000-fold traffic volumes, 100 billion connected wireless devices, and diversified use cases as well as quality of service (QoS) requirements (e.g., reliability, latency, data rates, coverage, security, and privacy) of multimedia applications by 2020. Recently, a number of research challenges including the explosive growth in mobile traffic volumes, unprecedented connected devices, and diversified use cases have been identified for the 5G RAN systems. In addition, specific technologies such as multitier communication, massive MIMOs, mmWave backhauling, extreme densifications of nodes (UDNs), full-duplex communications (FDCs), and energy harvesting techniques have emerged in the literature to resolve some of these challenges of the 5G RAN systems. However, the research activities defining specific technical advancements for 5G RAN systems are yet to continue in the next half decade before specifications for standardization and commercialization are concluded. Motivated by the limited number of existing surveys of such technical advancements in a broader perspective (i.e., interference, spectrum-efficient, and energy-efficient management schemes), this paper seeks to take stock of state-of-the-art (SOTA) on such technical developments. Our attention focuses on relevant radio interference and resource management (RIRM) schemes that have been proposed in the last five years. Our contribution lies in the analysis, synthesis, and summarized alignments of the conventional RIRM schemes toward overcoming the identified challenges for the $5G$ RAN systems. Finally, the review highlights a number of open research issues deduced from recently proposed RIRM schemes.

Internet of things (IoT) is a new emerging technology with no human control, a new era where it’s about creating values in a connected society with enhanced services and sharing data more than ever to improve our lifestyle. The massive growth of IoT connected devices generates huge amount of data which creates different challenges in terms of storage and processing capacity, network management and especially data security and privacy as a security breaches may have severe consequences such as life threatening. Combining IoT with other trending technologies such as Software Defined Network (SDN) and Cloud reveals so important because of the benefits they are offering to overcome these challenges. In this paper we will present a security proposal for IoT based on SDN and Cloud integration.

Graph cut segmentation provides a platform to analyze images through a global segmentation strategy, and as a result of this, it has gained a wider acceptability in many interactive and automatic segmentation fields of application, such as the medical field. The graph cut energy function has a parameter that is tuned to ensure that the output is neither oversegmented (shrink bias) nor undersegmented. Models have been proposed in literature towards the improvement of graph cut segmentation, in the context of interactive and automatic cell segmentation. Along this line of research, the graph cut parameter has been leveraged, while in some instances, it has been ignored. Therefore, in this work, the relevance of graph cut parameter on both interactive and automatic cell segmentation is investigated. Statistical analysis, based on F1 score, of three publicly available datasets of cells, suggests that the graph cut parameter plays a significant role in improving the segmentation accuracy of the interactive graph cut than the automatic graph cut.

International audience; The Internet of Things (IoT) refers to the domain of physical/logical objects connected to the Internet that can be accessible anywhere and anytime, giving the access policies. Web objects and services can be seen as distributed and cooperative agents that need to collaborate in order to reach advanced functionalities and also to optimize the overall quality offered to the end users. However, the number of the services that can be supplied through these devices on the Internet will increase more and more. In this context, performing the composition and selection of the best services according to quality-of-service (QoS) criteria become a complex task, especially when dealing with global service selection to satisfy global constraints of users in decentralized environments. For this purpose, we propose a distributed and optimal QoS selection approach based on the multiagent paradigm and the distributed constraint optimization problem (DCOP) formalism. Hence, we revisited a DCOP technique for developing an efficient algorithm: the DPOP4QoS implemented under a real-time Web protocol for IoT communication, the extensible messaging and presence protocol. The proposed algorithm takes into account the specificities of the service composition context and the satisfaction of the global user constraints. This paper also presents a set of experiments conducted under realistic distributed environment to evaluate the performance of the proposed algorithm.

The added benefits brought by the advent of the Vehicular network (VN) technology have stimulated a lot of hope in the area emergent transportation industries. Two most important factors that have motivated and contributed to the development, design and implementation of the VN standards include the need to ensure safety and the need to consider road accident avoidance strategies. However, the innate dynamic and the high topological mobility of the nodes in Vehicular Ad Hoc Networks (VANETs) raise complex and challenging issues with the standard. One of the complexities is the problem posed by Doppler effect (DE) resulting from the high mobility of the VANET nodes. In an attempt to compensate the induced Doppler shift (DS), the Automatic Doppler shift adaptation (ADSA) method was recently introduced to combat DE in a VANET. ADSA proved to be more resilient and effective in term of Bit error rate (BER). Moreover, for realistic applications, BER tests alone are insufficient. Therefore, in this work, a thorough analysis of the method is explored and the strength of the refined ADSA method is evaluated in terms of throughput, elapsed time, packet loss, model efficiency and data transfer rate. These metrics are used to perform a comparative analysis of ADSA versus adaptive modulation code (AMC) and auto-rate fallback (ARF). Results from the analysis shows that the ADSA approach is very effective and has a strong robustness compared to ARF and AMC with up to 300–700 % improvement in throughput and a 60–75 % reduction in consumed time.

In this paper the authors discuss the modelling and design of an augmented reality platform for disabled wheeled mobility studies. This design consists of a virtual environment with two degrees of freedom motion platform and integrated ground contact force feedback. Differential drive mobility users continue to be in touch with reality on their own mobility aid while interacting with virtual objects. The major domain related to the differential drive mobility of the disabled members of society which coincides with the use of manual wheelchairs, electric wheelchairs and mobility scooters. In order to account for environmental and dynamic effects, the wheeled mobility user needs to map the intended trajectory into the virtual world. Motion and inertia force feedback produced on the augmented simulator give the users a haptic sensory stimulus input regarding spatial movement and ground contact forces. The main objective is to model and design an augmented reality platform with real world kinematic and dynamic properties to place a wheeled mobility user closer to real world encounters. The use of the designed augmented reality platform will be beneficial to disabled wheeled mobility users in need of occupational therapist training and evaluation.

Multiple sinks routing is envisioned as a possible solution to the ‘bottleneck’ research problem in Wireless Sensor Networks (WSN). In addition to focusing on minimizing the energy consumption in a WSN, it is also equally important to design routing protocols that fairly and evenly distribute the network traffic; in order to prolong the network life time and improve its scalability. Gradient Based Routing (GBR) techniques such as the Generic GBR (GBR-G) and the Competing-GBR (GBR-C) have been previously proven to be energy efficient in single sink WSNs. These methods consider that each sensor node constructs a gradient with respect to a unique base station. The drawback of this approach is that, due to the position of sensor nodes next to the sink, their energy is usually overused compared to the one of the other sensor nodes in the network. To overcome this, this paper introduces enhanced GBR-G and GBR-C routing approaches (GB-GBR and CB-GBR) which consider the definition of a new gradient model to maximize network lifetime. In the proposed new approach, the GB-GBR and CB-GBR techniques not only consider the selection of the highest gradient link but also the link that avoids the most overloaded sensor nodes when forwarding packets. Using OMNET++ simulation and the MiXiM framework, it is shown that proposed GB-GBR and CB-GBR approaches achieve better performance in terms of network lifespan when compared to the single sink GBR-G and GBR-C approaches respectively.

A switching scheme based on Takagi-Sugeno fuzzy inference system is proposed in this paper to address the problems of poor transient response, rapid oscillation of plant output and long duration of switching time associated with the multiple model predictive control (MMPC) based on hard switching. A set of piecewise linear models is used to represent the system under consideration at different operating regimes. Corresponding MPC local controller is developed for each model. At each instant, the fuzzy switching system selects the appropriate model/controller pair for the system. The proposed MMPC strategy is applied to a coagulation chemical dosing unit for water purification plants. Simulation results of the proposed control scheme are promising and positive.

Over the last decade vehicle-to-vehicle (V2V) communication has received a lot of attention as it is a crucial issue in intravehicle communication as well as in Intelligent Transportation System (ITS). In ITS the focus is placed on integration of communication between mobile and fixed infrastructure to execute road safety as well as nonsafety information dissemination. The safety application such as emergence alerts lays emphasis on low-latency packet delivery rate (PDR), whereas multimedia and infotainment call for high data rates at low bit error rate (BER). The nonsafety information includes multimedia streaming for traffic information and infotainment applications such as playing audio content, utilizing navigation for driving, and accessing Internet. A lot of vehicular ad hoc network (VANET) research has focused on specific areas including channel multiplexing, antenna diversity, and Doppler shift compensation schemes in an attempt to optimize BER performance. Despite this effort few surveys have been conducted to highlight the state-of-the-art collection on Doppler shift compensation schemes. Driven by this cause we survey some of the recent research activities in Doppler shift compensation schemes and highlight challenges and solutions as a stock-taking exercise. Moreover, we present open issues to be further investigated in order to address the challenges of Doppler shift in VANETs.

Vehicular Adhoc Networks (VANET) was created to facilitate and enable inter vehicular communication. One of the most challenging problems affecting the feasibility of VANET is the problem of high mobility of the nodes resulting in an induced Doppler Shift (DS) in the carrier frequency at the receiver end. The work presented in this paper proposes a solution to mitigate the effect of DS affecting the VANET environment. Based on the Direct Derivation Method (DDM) derived from the principle of communication theory, an Improved DDM (IDDM) concept, analysis and design is developed and explored. Several critical tests are performed and it was demonstrated that not only the IDDM response exhibits the BER threshold limit described by the IEEE 802.11 standard; the approach offers for the same SNR value, a throughput improvement of up to 66.66 % and an improved BER response of more than 25 % compared to the DDM and Constant (Cte). A performance analysis also demonstrated that not only IDDM performs faster, it equally offers considerable improvement in term of efficiency over the DDM approach.

Brain Computer Interfaces (BCI) provide alternative communication pathways between the human brain and artificial agents and could restore mobility and improve quality of life to disable patients. Research in this field is evolving prompting researchers to render the technology available for practical usage. The following paper presents a BCI real time implementation framework in Matlab/Simulink which could provide researchers a simple usable short-cutting tool to test models and algorithms benefiting from resourceful toolboxes in the Matlab environment with little difficulty in the implementation process. The proposed platform targets endogenous BCIs only, provided that electrode cap can be interfaced with Simulink. The Simulink models designed, additional software packages and media are freely accessible online [1].

Maintaining robust tracking and positioning integrity in harsh environments is a real challenge for GNSS receivers. This paper considers three possible tracking and navigation solutions to address this challenge. Firstly, a vector tracking loop (VTL) capable of using correlator-based multipath detectors and other signal quality indicators to exclude contaminated satellites from the PVT calculation is proposed. Secondly, an adaptive scalar-vector tracking loop (STL-VTL) that continuously switches between STL and VTL modes based on the state of signal quality indicators is considered. Lastly, a conjoint scalar-vector tracking scheme which maintains STL and VTL modes simultaneously then chooses the measurements to be fed to the navigator based on the state of quality indicators is suggested. To evaluate the performance of the proposed solutions, the trajectory and environment simulator developed by the German Aerospace Center (DLR) is used to simulate GPS signals in harsh suburban and urban conditions. The exclusion of multipath contaminated satellites from PVT calculation improves PVT accuracy and tracking performance. The adaptive and conjoint STL-VTL schemes allow, each in its way, to exploit the advantages of both STL and VTL regarding positioning reliability and tracking robustness.

International audience; This brief deals with a modular controller using a fast terminal sliding mode approach for articulated systems represented by exoskeletons to perform flexion/extension movements. The proposed controller supposes that all model functions are unknown except classical properties related to the boundedness of some parameters. On the other hand, the disturbances are assumed to be bounded. It permits finite-time convergence to the desired trajectories in both position and velocity. The system is divided into several subsystems and a particular unit controls each subsystem. A supervisor using the Lyapunov approach ensures the closed-loop stability of the overall system. The proposed robust controller has been applied in a real-time application to drive an upper limb exoskeleton having 3 DOF. The used device worn by a healthy subject performs flexion/extension movements often practiced for rehabilitation purposes. A strict security protocol, which is generally used by therapists, has been respected. The obtained results are satisfactory and prove the effectiveness and the robustness of the proposed controller.

Despite its low computational cost, the Gradient Based Routing (GBR) broadcast of interest messages in Wireless Sensor Networks (WSNs) causes significant packets duplications and unnecessary packets transmissions. This results in energy wastage, traffic load imbalance, high network traffic, and low throughput. Thanks to the emergence of fast and powerful processors, the development of efficient network coding strategies is expected to enable efficient packets aggregations and reduce packets retransmissions. For multiple sinks WSNs, the challenge consists of efficiently selecting a suitable network coding scheme. This article proposes a Cooperative and Adaptive Network Coding for GBR (CoAdNC-GBR) technique which considers the network density as dynamically defined by the average number of neighbouring nodes, to efficiently aggregate interest messages. The aggregation is performed by means of linear combinations of random coefficients of a finite Galois Field of variable size GF(2S) at each node and the decoding is performed by means of Gaussian elimination. The obtained results reveal that, by exploiting the cooperation of the multiple sinks, the CoAdNC-GBR not only improves the transmission reliability of links and lowers the number of transmissions and the propagation latency, but also enhances the energy efficiency of the network when compared to the GBR-network coding (GBR-NC) techniques.

A multiple model predictive control (MMPC) scheme based on fuzzy weighting is proposed to control the coagulation chemical dosing unit for water treatment plants. The system is represented by a set of linearised models, where each model corresponds to one of the operating regions of the dosing unit. MPC local controller is designed for each local model. The local control input sequence of each model/controller pair in the scheme is aggregated by fuzzy weighting into a global control input to regulate the surface charge and pH level of the dosing unit. The parameters of the fuzzifiers are fine tuned to improve the performance of the proposed control scheme using the nonlinear curve fitting optimisation technique. Simulation results on a process model of the coagulation chemical dosing unit show the ability of the proposed fuzzy weighting MMPC algorithm to transit smoothly from one operating region to another effectively better than switching and weighting MMPC schemes.

In this paper, a fuzzy model predictive control (FMPC) strategy is proposed to regulate the output variables of a coagulation chemical dosing unit. A multiple-input, multiple-output (MIMO) process model in form of a linearised Takagi–Sugeno (T–S) fuzzy model is derived. The process model is obtained through subtractive clustering from the plant's data set. The MIMO model is described by a set of coupled multiple-input, single-output models (MISO). In the controller design, the T–S fuzzy model is applied in combination with the nonlinear model predictive control (MPC) algorithm. The results show that the proposed controller has good set-point tracking when compared with nonlinear MPC and adequate disturbance rejection ability required for efficient coagulation control and process optimisation in water treatment operations.

This paper proposes a mathematical maintenance model that analyses the effect of maintenance on the survival probability of medical equipment based on maintenance history and age of the equipment. The proposed model is simulated in Scilab using real data extracted from maintenance history of anaesthesia machine from Draeger. The analysis using survival approach reveals that conducting preventive maintenance on the selected medical equipment had a positive impact on survival of equipment. The model is then used to analyse the cost of maintenance scenarios, and an appropriate scenario is proposed for anaesthesia machine. A new failure-cost model is developed, which may be used to calculate the number of failures of equipment and the annual maintenance cost. The proposed models may be used as a planning tool for selecting maintenance strategies for various medical equipments. Copyright © 2014 John Wiley & Sons, Ltd.

In Reconfigurable Manufacturing Systems (RMSs), the structure of the system can be changed during execution of the system. This reconfiguration can be motivated by a new requirement in the production process, or to avoid some problems caused by machines breakdowns. These systems offer a high flexibility leading to more productivity and efficiency. However, their design is more complicated implying new techniques and paradigms. The use of formal high level Petri Nets offers the ability to design these systems and to analyse or prove their properties. In this paper, we apply Reconfigurable Object Nets (RONs) for the modelling, simulation and analysis of reconfigurable manufacturing systems. We propose a formal approach, where the reconfiguration is specified as graph transformations, the simulation is realized using the RON-tool, and the analysis exploits some software tools such as TINA-tool and PIPE-tool.

Purpose – In this paper, a new method to improve the performance of particle swarm optimization is proposed. Design/methodology/approach – This paper introduces hypothesis testing to determine whether the particles trap into the local minimum or not, then special re-initialization was proposed, finally, some famous benchmarks and constrained engineering optimization problems were used to test the efficiency of the proposed method. In the revised manuscript, the content was revised and more information was added. Findings – The proposed method can be easily applied to PSO or its varieties. Simulation results show that the proposed method effectively enhances the searching quality. Originality/value – This paper proposes an adaptive particle swarm optimization method (APSO). A technique is applied to improve the global optimization performance based on the hypothesis testing. The proposed method uses hypothesis testing to determine whether the particles are trapped into local minimum or not. This research shows that the proposed method can effectively enhance the searching quality and stability of PSO.

The time-varying nature of the parameters influencing the Quality of service (QoS) of real-time applications means the Call Admission Control (CAC) in LTE networks has to deal with some degree of uncertainty. The existing approaches fail to properly manage this uncertainty. A model is proposed to effectively manage real-time traffic via the deployment of a Type-1 Fuzzy Logic Controllers (FLCs). Compared to the Quality Index based approach, the obtained results present notable improvements regarding the call rejection and call blocking probabilities in a 4G LTE Network.

Vehicular network (VN) technologies have become an attractive area of attention all over the world. Two factors that have contributed in the development, design and implementation of the VN standards include the need to ensure safety and the need to consider road accident avoidance strategies. However, the innate dynamic and the high topological mobility of the nodes in Vehicular Ad Hoc Networks (VANETs) raise complex and challenging issues with the standard. One of the complexities is the problem posed by Doppler Effect (DE) resulting from the high mobility of the VANET nodes. In an attempt to compensate the induced Doppler Shift (DS), Automatic Doppler Shift Adaptation (ADSA) was recently introduced to combat DE in a VANET. ADSA proved to be more resilient and effective in term of Bit Error Rate (BER). Moreover, for realistic applications, BER tests alone are insufficient. Therefore, this work explores the strength of the refined ADSA method in terms of throughput and presents a comparative analysis of ADSA versus Adaptive Modulation Code (AMC) and Auto-Rate Fallback (ARF). Results from the analysis shows that the ADSA approach demonstrates strong robustness compared to AMC and ARF with up to 44 to 55% improvement in throughput and a 174 to 182% reduction in consumed time.

PurposeStudying the availability of medical equipment based on various maintenance types has been a major concern for hospitals. Most of the methodologies used are empirical and few are based on mathematical modelling. The objective of this paper is to present a mathematical maintenance model that analyses the effect of maintenance on the survival probability of medical equipment based on maintenance history and age of that equipment.Design/methodology/approachA global model is proposed to measure the probability of equipment being available using real data extracted from maintenance history of infusion pumps and ventilators and analysed using Matlab. To confirm the validity of the developed model, the survival analysis approach is used to develop a model that measures the survival of equipment as a function of maintenance and age of equipment. The method is first tested using simulated data and the findings confirm the validity of the proposed approach.FindingsThe analysis using survival approach reveals that conducting preventive maintenance (PM) on the selected medical equipment had an impact on survival of equipment. However, the manufacturer's recommended PM intervals do not correlate to the failure rate encountered. This will contribute to the debate on PM manufacturer's recommended intervals and might lead to the revision of maintenance strategies implemented by hospitals and clinical engineering (CE) practitioners.Research limitations/implicationsAlthough the data collected for the infusion pumps were quite sufficient, that collected for the ventilators were more limited. The major difficulty is that of the availability of historical maintenance data and the effect of user errors may cause uncertainty in the analysis. A closer collaboration with the medical professional should facilitate the recording and access to such information.Practical implicationsThe use of mathematical modelling to analyse the effect of maintenance on the survival of medical equipment is a beneficial tool that is not exploited in the medical equipment industry. It will provide CE practitioners with scientific tool to analyse the effect of PM on the survival of medical equipment.Originality/valueThis paper presents a mathematical approach to analyse the effect of maintenance on the survival of medical equipment, which is crucial in the assessment of maintenance strategies implemented in the medical equipment industry.

In a vehicular network, all mobile nodes in communication are affected by Doppler Effect (DE) caused by the mobility of node. DE contributes considerably to the channel degradation in a Vehicular Ad-hoc Network (VANET). In this paper, a novel Modulation Code Scheme (MCS) adaptation technique to combat DE is presented. This method called Automatic Doppler Shift Adaptation (ADSA) is a new way of optimizing the transmission system medium based on extensive simulation. The performance evaluation of the ADSA method is explored and compared to the Binary Phase Shift Keying (BPSK) with rate 1/2. Some published works have shown that a higher DE can only be combated using BPSK rate 1/2. However, results from simulations clearly demonstrate that the ADSA method outperforms BPSK rate 1/2 MCS under various channel condition in terms of Bit Error Rate (BER), and data rate which results in improved throughput.

Wireless ad-hoc networks are characterized by a collection of wireless mobile nodes that dynamically form a temporary network without the use of any pre-defined network infrastructure or centralized administration. They have attributes such multi-hop wireless connectivity, continuously changing topology and easy deployment. The increasing demands that are made by users pose a challenge in the improvement of the quality of wireless ad-hoc networks. The performance of wireless ad-hoc networks depend on the efficiency of routing protocols, especially the routing metric operating within it. In this paper, the performance of the recently proposed routing metric Interference Bandwidth Adjusted Expected transmission count (IBETX) metric is compared with two prominent existing metrics, the Expected transmission count (ETX) and the Inverse Expected Transmission count (InvETX) over the Destination Sequenced Distance Vector (DSDV) routing protocol. The performance differentials are analysed using varying packet rates and node densities. From the simulations results, IBETX metric outperforms the ETX and InvETX metric because it implements the bandwidth sharing mechanism and makes use of the information available at the MAC layer. It can also be noted that it is very sensitive to data traffic rate. It performs well in low and medium node densities.

Emerging Ambient Assisted Living (AAL) applications, as a part of AmI applications, deal essentially with health- care related applications such as assistance to the elderly and handicapped persons, emergency services. Several ap- proaches and techniques have been proposed, providing formal languages modeled with ontologies (e.g. OWL-S, WSMO) that describe in semantic way the environment. In this paper, relevant challenges of the current AAL ap- plication development, with a focus on the formal specification and verification are discussed. A formal system which enable to specify a semantic model represented by an upper ontology is presented. The innovative aspect of the proposed model concerns the use of a constructive description logic.