A critical analysis of research potential, challenges and future directives in industrial wireless sensor networks

In recent years, Industrial Wireless Sensor Networks (IWSNs) have emerged as an important research theme with applications spanning a wide range of industries including automation, monitoring, process control, feedback systems and automotive. Wide scope of IWSNs applications ranging from small production units, large oil and gas industries to nuclear fission control, enables a fast-paced research in this field. Though IWSNs offer advantages of low cost, flexibility, scalability, self-healing, easy deployment and reformation, yet they pose certain limitations on available potential and introduce challenges on multiple fronts due to their susceptibility to highly complex and uncertain industrial environments. In this paper a detailed discussion on design objectives, challenges and solutions, for IWSNs, are presented. A careful evaluation of industrial systems, deadlines and possible hazards in industrial atmosphere are discussed. The paper also presents a thorough review of the existing standards and industrial protocols and gives a critical evaluation of potential of these standards and protocols along with a detailed discussion on available hardware platforms, specific industrial energy harvesting techniques and their capabilities. The paper lists main service providers for IWSNs solutions and gives insight of future trends and research gaps in the field of IWSNs

Energy efficient scheduling and allocation of tasks in sensor cloud

Wireless Sensor Network (WSN) is a class of ad hoc networks that has capability of self-organizing, in-network data processing, and unattended environment monitoring. Sensor-cloud is a cloud of heterogeneous WSNs. It is attractive as it can change the computation paradigm of wireless sensor networks. In Sensor-Cloud, to gain profit from underutilized WSNs, multiple WSN owners collaborate to provide a cloud service. Sensor Cloud users can simply rent the sensing services which eliminates the cost of ownership, enabling the usage of large scale sensor networks become affordable. The nature of Sensor-Cloud enables resource sharing and allows virtual sensors to be scaled up or down. It abstracts different platforms hence giving the impression of a homogeneous network. Further in multi-application environment, users of different applications may require data based on different needs. Hence scheduling scheme in WSNs is required which serves maximum users of various applications. We have proposed a scheduling scheme suitable for the multiple applications in Sensor Cloud. Scheduling scheme is based on TDMA which considers fine granularity of tasks. The performance evaluation shows the better response time, throughput and overall energy consumption as compared to the base case we developed. On the other hand, to minimize the energy consumption in WSN, we design an allocation scheme. In Sensor Cloud, we consider sparsely and densely deployed WSNs working together. Also, in a WSN there might be sparsely and densely deployed zones. Based on spatial correlation and with the help of Voronoi diagram, we turn on minimum number of sensors hence increasing WSN lifetime and covering almost 100 percent area. The performance evaluation of allocation scheme shows energy efficiency by selecting fewer nodes in comparison to other work --Abstract, page iv

Towards Massive Machine Type Communications in Ultra-Dense Cellular IoT Networks: Current Issues and Machine Learning-Assisted Solutions

The ever-increasing number of resource-constrained Machine-Type Communication (MTC) devices is leading to the critical challenge of fulfilling diverse communication requirements in dynamic and ultra-dense wireless environments. Among different application scenarios that the upcoming 5G and beyond cellular networks are expected to support, such as eMBB, mMTC and URLLC, mMTC brings the unique technical challenge of supporting a huge number of MTC devices, which is the main focus of this paper. The related challenges include QoS provisioning, handling highly dynamic and sporadic MTC traffic, huge signalling overhead and Radio Access Network (RAN) congestion. In this regard, this paper aims to identify and analyze the involved technical issues, to review recent advances, to highlight potential solutions and to propose new research directions. First, starting with an overview of mMTC features and QoS provisioning issues, we present the key enablers for mMTC in cellular networks. Along with the highlights on the inefficiency of the legacy Random Access (RA) procedure in the mMTC scenario, we then present the key features and channel access mechanisms in the emerging cellular IoT standards, namely, LTE-M and NB-IoT. Subsequently, we present a framework for the performance analysis of transmission scheduling with the QoS support along with the issues involved in short data packet transmission. Next, we provide a detailed overview of the existing and emerging solutions towards addressing RAN congestion problem, and then identify potential advantages, challenges and use cases for the applications of emerging Machine Learning (ML) techniques in ultra-dense cellular networks. Out of several ML techniques, we focus on the application of low-complexity Q-learning approach in the mMTC scenarios. Finally, we discuss some open research challenges and promising future research directions.Comment: 37 pages, 8 figures, 7 tables, submitted for a possible future publication in IEEE Communications Surveys and Tutorial

Wireless Vehicular Communication Based Solution for Road Traffic Efficiency

Wireless vehicular communications is a cutting edge set of technologies driven by the vision of providing a suite of original applications, and supported by emerging standards such as IEEE 802.11p. In turn the popularity of these applications is one of the key factors, which will drive the uptake of these vehicular communications technologies and ultimately determine their market success. Applications for vehicular communications can be placed in three main categories - Traffic Safety, Traffic Efficiency and Value-added Services (e.g. Infotainment/Business). Our work focuses on the provision of traffic efficiency services as we believe they offer an immediate benefit and can be adopted quickly by a large number of potential users. Satellite navigation systems provide a ready made deployment platform for these types of services and have already proven popular (14.4 million portable satellite navigation systems sold in Western Europe in 2007). There is also an existing trend toward complementing satellite navigation-related technology with local area wireless communications (by 2013 34% of all portable navigation devices will feature wireless cards 2). Our emphasis is on an infrastructure-based approach as this allows early adopters of wireless enabled satellite navigation devices to receive useful services from day one, regardless of the penetration level of the technology. This thesis describes Smart City, a novel framework, which purposes the use of wireless communication to make city life greener and more efficient. A major contribution to this framework is the proposed intelligent traffic management module. A route management service, which is powered by a best route selection algorithm, is put forward as a prototypical traffic efficiency service for this module. The novel aspect is that the algorithm minimizes journey times and traffic congestion as well as fuel consumption and emissions. Testing has shown how the algorithm provides-shorter journey times, a reduction in fuel consumption and harmful emissions and also results in financial savings. We have proposed and implemented an infrastructure-based communication scheme that enables prioritization of services provided to vehicles