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Narrowband Internet-of-Things (NB-IoT) is a low-power wide area (LPWA) technology developed by the Third-generation Partnership Project (3GPP) with objective to enable a wide range of IoT devices, low cost device and low power in the 5G era. As the number of IoT devices continue to increase, the demand for the spectrum allocation grows proportionately. The NB-IoT spectrum allocation is limited from 180 KHz to 200 KHz and is not sufficient to accomodate the exponential surge in the size of the NB-IoT devices.Thus, the need to efficiently allocate the available spectrum to the NB-IoT devices. Furthermore, in an attempt to enhance the coverage in NB-IoT network, recent relevant studies (3GPP release 13) have introduced the concept of repeated transmission. Since repeated transmissions ensure coverage enhancement but cause spectrum wastage, the traditional resource allocation is not appropriate for NB-IoT network. Motivated by this research gap we propose a NB-Cognitive Radio-IoT (NB-CR-IoT) technique which integrates Cognitive Radio (CR) techniques into the operation of the conventional NB-IoT. The resulting architecture seeks to foster an efficient opportunistic spectrum access in distributed heterogeneous networks.We further formulate the resource allocation problem as a deep Q-learning solved by reducing the number of repeated transmissions and allocating more IoT devices in NB-IoT network. The results in this contribution indicate that DQN outperforms the traditional Q-learning algorithm.

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.

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.

Ambient environments involve a variety of smart devices. Communities of these devices form a ubiquitous information processing system to which the physical environment should provide an intuitive and flexible user interface. Indeed, due to the proliferation of the number of services in ubiquitous environments, the problem of dynamic service composition becomes a complex task. Our work tries to solve the problem of service composition based on AI planning. The approach used in our framework is based on a temporal reasoning system called Event Calculus. This formalism is used to generate an action plan to a symbolic level from a composition plan described in XML modeling the behavior of the composition of services. Event Calculus has a double interest to propose a solution to the problem of temporal persistence and to provide a usable technique for planning. The framework for service composition using Event Calculus includes mechanism for the service selection, based on an estimation of the reputation for abstract services and the Quality of Service (QoS).

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.

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.

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.

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.

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.

The 4x4 perfect space-time block code (STBC) is one type in a family of perfect STBCs that have full rate, full diversity, a non- vanishing constant minimum determinant that improves spectral efficiency, uniform average transmitted energy per antenna, and good shaping. These codes suffer very high complexity maximum likelihood (ML) decoding. The exhaustive ML decoding of the 4x4 perfect STBC for instance has complexity of O(N16), with N being the size of the underlying QAM constellation. This paper suggests a fast decoding algorithm for the 4x4 perfect STBC that reduces decoding complexity from O(N16) to O(N8). The algorithm is based on conditional minimization of the ML decision metric with respect to one subset of symbols given another. This low complexity decoding approach is essentially ML because it suffers no loss in symbol error rate (SER) performance when compared to the exhaustive ML decoding.

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.

In the recent years Space Time Block Coding has attracted the attention of the research community in the quest of optimizing the use of the bandwidth to support today's wireless communications applications. To achieve this aim, STBC exploits the spatial diversity. However, complexity challenges exacerbate when more than two transmit and more than one receive antennas are used. This paper presents the implementation of a low-complexity Maximum Likelihood (ML) decoding complexity of the Double Alamouti 4x2 STBC. The design exploits and takes advantage of the enhanced processing capability of an FPGA to achieve this aim. The scheme was first coded in MATLAB, then in ModelSim. Both MATLAB and VHDL performance results have been presented in terms of Symbol Error Rate (SER) versus Signal to Noise ratio (SNR) for 4-QAM.

This paper presents a review of recent developments in the area of STBC decoding particularly parallel decoding of full-rate full-diversity STBCs toward real-time 4G wireless communications. After reviewing some parallel STBC decoding techniques and presenting one of the most promising types of parallel processors suitable for the 4G SDR the SIMD processor, the paper shows that parallel decoding of the Golden Code on the ClearSpeed CSX700 SIMD processor achieves a speedup of up to 30 times. The paper highlights the potential to achieve real-time decoding of high-rate STBCs with the use of robust parallel processors.

Billions of devices are now integral part of the day-to-day routine of people but they are sometime incompatible with one another because they employ divergent protocols. The introduction of semantics has been a key enabler towards proper and universal descriptions of connected objects as well as the relationship between them, removing relational ambiguities and improving context-awareness. Virtual objects (VOs) are semantic descriptions of the aggregated native capabilities of physical devices. And as such, they can be readily upgraded over time in order to dynamically suit the processes they are involved with because the context information that is present within them is not necessarily static in time. The original VOs often need to be semantically enriched with more contextual details as they interact with applications and other reasoning engines, where they often evolve into composite virtual objects (cVOs) or rich virtual objects (rVOs) that are more intelligent and interoperable. The complexity of handling large volumes of streaming data increases in the presence of uncertainty caused by incomplete, altered, noisy and/or unstructured information. Context brokering has been a growing trend over the past few years where brokers have been used to discover context and to facilitate processes between service requesting and service providing entities. Several context brokers have been proposed but they do not offer a scalable and optimised brokering algorithm. This necessitates the development of novel ontologies where more functional and non-functional descriptions are used to reduce uncertainties and enrich the content of VOs with the objective of creating a truly automated networking environment.

The Relative Coordinate Rumor Routing (RCRR) is a hybrid routing protocol based on a straight line routing scheme proposed to overcome the drawbacks of the Rumor Routing (RR) protocol which is based on a random walk approach. This paper presents a performance study of the RCRR protocol for Wireless Sensor Networks. The main objective is to evaluate the performances of the new RCRR protocol under different situations and considering different factors such as network size or the impact of the positions of the Beacon nodes originally placed along the horizontal and the vertical line of the 4 cardinal points. Through the presented scenarios and simulations, it is shown that the energy consumption is much more in some cases and much less in other cases. Based on this study, guidelines can be deduced to improve the behavior of the RCCR routing protocol.