A Routing Algorithm for Extending Mobile Sensor Network’s Lifetime using Connectivity and Target Coverage

In this paper, we propose an approach to improving the network lifetime by enhancing Network CONnectivity (NCON) and Target COVerage (TCOV) in randomly deployed Mobile Sensor Network (MSN). Generally, MSN refers to the collection of independent and scattered sensors with the capability of being mobile, if need be. Target coverage, network connectivity, and network lifetime are the three most critical issues of MSN. Any MSN formed with a set of randomly distributed sensors should be able to select and successfully activate some subsets of nodes so that they completely monitor or cover the entire Area of Interest (AOI). Network connectivity, on the other hand ensures that the nodes are connected for the full lifetime of the network so that collection and reporting of data to the sink node are kept uninterrupted through the sensor nodes. Keeping these three critical aspects into consideration, here we propose Socratic Random Algorithm (SRA) that ensures efficient target coverage and network connectivity alongside extending the lifetime of the network. The proposed method has been experimentally compared with other existing alternative mechanisms taking appropriate performance metrics into consideration. Our simulation results and analysis show that SRA performs significantly better than the existing schemes in the recent literature

Algorithms for data-gathering in wireless sensor networks

Wireless sensor networks consist of a large number of small battery powered sensor nodes with limited energy resources which are responsible for sensing, processing, and transmitting the monitored data. Once deployed, the sensor nodes are normally inaccessible to the user, and thus replacement of the battery is generally not feasible. A major concern in designing and operating dense Wireless Sensor Networks (WSNs) is the energy-efficiency. Hierarchical clustering and cross-layer optimization are widely accepted as effective techniques to ameliorate this concern. We propose two different novel energy efficient algorithms to gather data from sensor nodes. Energy-Efficient Media Access Control (EE-MAC) protocol is the first algorithm, which has excellent scalability and performs well for both small and large sensor networks. We will also provide a theoretical analysis of the protocol and give guidelines on how to find the optimal protocol parameters such as the number of clusters. In addition, we develop and analyze a novel and scalable Spiraled Algorithm for Data-gathering (SAD) that periodically selects cluster heads according to their geographic locations and residual energy by sorting nodes on virtual spirals. Theoretical analysis and simulation results show that SAD can achieve as much as a factor of three prolonging network lifetime compared with other conventional protocols like LEACH especially when the network is large. Moreover, SAD is also able to distribute energy dissipation evenly throughout the sensors such that 80% of the nodes run out of batteries in the last 20% of the network lifetime

A Comprehensive Approach to WSN-Based ITS Applications: A Survey

In order to perform sensing tasks, most current Intelligent Transportation Systems (ITS) rely on expensive sensors, which offer only limited functionality. A more recent trend consists of using Wireless Sensor Networks (WSN) for such purpose, which reduces the required investment and enables the development of new collaborative and intelligent applications that further contribute to improve both driving safety and traffic efficiency. This paper surveys the application of WSNs to such ITS scenarios, tackling the main issues that may arise when developing these systems. The paper is divided into sections which address different matters including vehicle detection and classification as well as the selection of appropriate communication protocols, network architecture, topology and some important design parameters. In addition, in line with the multiplicity of different technologies that take part in ITS, it does not consider WSNs just as stand-alone systems, but also as key components of heterogeneous systems cooperating along with other technologies employed in vehicular scenarios

Energy-Efficient Self-Organization Protocols for Sensor Networks

A Wireless Sensor Network (WSN, for short) consists of a large number of very small sensor devices deployed in an area of interest for gathering and delivery information. The fundamental goal of a WSN is to produce, over an extended period of time, global information from local data obtained by individual sensors. The WSN technology will have a significant impact on a wide array of applications on the efficiency of many civilian and military applications including combat field surveillance, intrusion detection, disaster management among many others. The basic management problem in the WSN is to balance the utility of the activity in the network against the cost incurred by the network resources to perform this activity. Since the sensors are battery powered and it is impossible to change or recharge batteries after the sensors are deployed, promoting system longevity becomes one of the most important design goals instead of QoS provisioning and bandwidth efficiency. On the other hand the self-organization ability is essential for the WSN due to the fact that the sensors are randomly deployed and they work unattended. We developed a self-organization protocol, which creates a multi-hop communication infrastructure capable of utilizing the limited resources of sensors in an adaptive and efficient way. The resulting general-purpose infrastructure is robust, easy to maintain and adapts well to various application needs. Important by-products of our infrastructure include: (1) Energy efficiency: in order to save energy and to extend the longevity of the WSN sensors, which are in sleep mode most of the time. (2) Adaptivity: the infrastructure is adaptive to network size, network topology, network density and application requirement. (3) Robustness: the degree to which the infrastructure is robust and resilient. Analytical results and simulation confirmed that our self-organization protocol has a number of desirable properties and compared favorably with the leading protocols in the literature

Smart Wireless Sensor Networks

The recent development of communication and sensor technology results in the growth of a new attractive and challenging area - wireless sensor networks (WSNs). A wireless sensor network which consists of a large number of sensor nodes is deployed in environmental fields to serve various applications. Facilitated with the ability of wireless communication and intelligent computation, these nodes become smart sensors which do not only perceive ambient physical parameters but also be able to process information, cooperate with each other and self-organize into the network. These new features assist the sensor nodes as well as the network to operate more efficiently in terms of both data acquisition and energy consumption. Special purposes of the applications require design and operation of WSNs different from conventional networks such as the internet. The network design must take into account of the objectives of specific applications. The nature of deployed environment must be considered. The limited of sensor nodes� resources such as memory, computational ability, communication bandwidth and energy source are the challenges in network design. A smart wireless sensor network must be able to deal with these constraints as well as to guarantee the connectivity, coverage, reliability and security of network's operation for a maximized lifetime. This book discusses various aspects of designing such smart wireless sensor networks. Main topics includes: design methodologies, network protocols and algorithms, quality of service management, coverage optimization, time synchronization and security techniques for sensor networks

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

A Survey on Routing Protocols for Large-Scale Wireless Sensor Networks

With the advances in micro-electronics, wireless sensor devices have been made much smaller and more integrated, and large-scale wireless sensor networks (WSNs) based the cooperation among the significant amount of nodes have become a hot topic. “Large-scale” means mainly large area or high density of a network. Accordingly the routing protocols must scale well to the network scope extension and node density increases. A sensor node is normally energy-limited and cannot be recharged, and thus its energy consumption has a quite significant effect on the scalability of the protocol. To the best of our knowledge, currently the mainstream methods to solve the energy problem in large-scale WSNs are the hierarchical routing protocols. In a hierarchical routing protocol, all the nodes are divided into several groups with different assignment levels. The nodes within the high level are responsible for data aggregation and management work, and the low level nodes for sensing their surroundings and collecting information. The hierarchical routing protocols are proved to be more energy-efficient than flat ones in which all the nodes play the same role, especially in terms of the data aggregation and the flooding of the control packets. With focus on the hierarchical structure, in this paper we provide an insight into routing protocols designed specifically for large-scale WSNs. According to the different objectives, the protocols are generally classified based on different criteria such as control overhead reduction, energy consumption mitigation and energy balance. In order to gain a comprehensive understanding of each protocol, we highlight their innovative ideas, describe the underlying principles in detail and analyze their advantages and disadvantages. Moreover a comparison of each routing protocol is conducted to demonstrate the differences between the protocols in terms of message complexity, memory requirements, localization, data aggregation, clustering manner and other metrics. Finally some open issues in routing protocol design in large-scale wireless sensor networks and conclusions are proposed