The conception of a wireless sensor network may present numerous challenges, such as scalability, reliability, longevity and timeliness. The existence of multiple sinks may increase the network reliability and facilitates the scalability. However, this improvement is dependent on the routing approach, that must be tailored to help achieving the desired performance goals.From this perspective, the objective of this work is to find ways of optimizing the communication in multi-sink wireless sensor networks considering the problems related to the scalability, longevity (network lifetime), reliability (packet delivery) and timeliness (latency). We investigate the trades among data delivery time and energy consumption as key metrics for communication quality and efficiency. For that matter, we propose different routing algorithms, covering all three main communciations schemes (unicast, anycast and multicast).The executed simulations show that our approaches are capable of optimizing the communication, especially in terms of latency and network lifetime. Experiments on the FIT IoT-Lab platform also provide meaningful insights of the performance of our multicast solution in real environment condition.
Wireless Sensor Networks came into prominence around the start of this millennium motivated by the omnipresent scenario of small-sized sensors with limited power deployed in large numbers over an area to monitor different phenomenon. The sole motivation of a large portion of research efforts has been to maximize the lifetime of the network, where network lifetime is typically measured from the instant of deployment to the point when one of the nodes has expended its limited power source and becomes in-operational - commonly referred as first node failure. Over the years, research has increasingly adopted ideas from wireless communications as well as embedded systems development in order to move this technology closer to realistic deployment scenarios. In such a rich research area as wireless sensor networks, it is difficult if not impossible to provide a comprehensive coverage of all relevant aspects. In this book, we hope to give the reader with a snapshot of some aspects of wireless sensor networks research that provides both a high level overview as well as detailed discussion on specific areas.
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.
This book presents nature inspired computing applications for the wireless sensor network (WSN). Although the use of WSN is increasing rapidly, it has a number of limitations in the context of battery issue, distraction, low communication speed, and security. This means there is a need for innovative intelligent algorithms to address these issues.The book is divided into three sections and also includes an introductory chapter providing an overview of WSN and its various applications and algorithms as well as the associated challenges. Section 1 describes bio-inspired optimization algorithms, such as genetic algorithms (GA), artificial neural networks (ANN) and artificial immune systems (AIS) in the contexts of fault analysis and diagnosis, and traffic management. Section 2 highlights swarm optimization techniques, such as African buffalo optimization (ABO), particle swarm optimization (PSO), and modified swarm intelligence technique for solving the problems of routing, network parameters optimization, and energy estimation. Lastly, Section 3 explores multi-objective optimization techniques using GA, PSO, ANN, teaching–learning-based optimization (TLBO), and combinations of the algorithms presented. As such, the book provides efficient and optimal solutions for WSN problems based on nature-inspired algorithms.
Wireless communication and sensor networks would form the backbone to create pervasive and ubiquitous environments that would have profound influence on the society and thus are important to the society. The wireless communication technologies and wireless sensor networks would encompass a wide range of domains such as HW devices such as motes, sensors and associated instrumentation, actuators, transmitters, receivers, antennas, etc., sensor network aspects such as topologies, routing algorithms, integration of heterogeneous network elements and topologies, designing RF devices and systems for energy efficiency and reliability etc. These sensor networks would provide opportunity to continuously and in a distributed manner monitor the environment and generate the necessary warnings and actions. However most of the developments have been demonstrated only in controlled and laboratory environments. So we are yet to see those powerful, ubiquitous applications for the benefit of the society. The conference and consequentially the proceedings would provide opportunity to the researchers to interact with other researchers and share their researches covering all the above areas. The proceedings of the conference thus covers the research work of different authors in the area of wireless sensor networks, wireless communications, devices, tools and techniques for WSN, and applications of wireless sensor networks. This book is beneficial for those researchers who are working in the area of wireless sensor networks, wireless communication, and developing applications of Wireless sensor networks.
Wireless sensor networks have gained much attention these last years thanks to the great set of applications that accelerated the technological advances. Such networks have been widely investigated and many books and articles have been published about the new challenges they pose and how to address them. One of these challenges is node mobility: sensors could be moved unexpectedly if deployed in an uncontrolled environment or hold by moving object/animals.Beyond all this, a new dimension arises when this mobility is controlled, i.e. if these sensors are embedded in robots. These robots cohabit with sensors and cooperate together to perform a given task collectively by presenting hardware constraints: they still rely on batteries; they communicate through short radio links and have limited capacities.In this book, we propose to review new challenges brought about by controlled mobility for different goals and how they are addressed in the literature in wireless sensor and Robot networks, ranging from deployment to communications.
Ad hoc networks, useful in situations where the deployment of any communication infrastructure is difficult or impossible, are formed by autonomous nodes via multihop wireless communications. Wireless sensor networks consist of spatially distributed sensors with both sensing and communication capabilities. These sensors collaboratively form multi-hop networks to facilitate various monitoring tasks. As a critical functionality to facilitate multi-hop communications in both types of wireless networks, the operations of routing usually need to consider multiple desirable but competing performance objectives, e.g., power conservation vs. hop count reduction, path efficiency vs. path redundancy, and tour length minimization vs. sensor coverage maximization. Convoluted tradeoff relationships exist among these conflictive objectives. This dissertation investigates tradeoffs among multiple competing routing objectives in three different networking scenarios, and designs efficient and adaptive routing schemes that balance conflictive optimization objectives. In the first work, we formulate the tradeoff between reducing forwarding power and end-to-end hop count in ad hoc multicast routing as an NP-hard constrained Steiner tree (CST) problem, and design a distributed heuristic algorithm termed CSTMAN that adopts the metaphor of Swarm Intelligence. CSTMAN concurrently constructs a CST, performs dynamic transmission power assignment at non-leaf forwarding nodes, and strives to minimize the total transmission power of the multicast tree subject to a given hop count constraint. We also address the similar tradeoff issue in ad hoc unicast routing and design a unicast routing protocol termed CEDAR. Realistic network simulations validate the effectiveness of both protocols and demonstrate the tradeoff relationship between the end-to-end hop count constraint and the total forwarding power. In the second work, we aim at the competing objectives of efficiency (in terms of single path and hop count) and robustness (in terms of path redundancy) in converge-cast routing from a large number of sensors to the data sink(s) for wireless sensor networks, and design two localized protocols that draw inspirations from the biological system of slime mold. Specifically, the path 'growth' protocol treats data sources and sinks as Singular Potentials, and establishes connectivity from the data sinks to all the data sources based on the singular potential model. In addition, by adapting an existing tube dynamics model, the path 'evolution' protocol iteratively computes network connectivity based on local tube flux distributions. Through realistic network simulations and in comparison with ideal closed form or numerical solutions to the corresponding mathematical models, we validate the effectiveness of both protocols as well as the efficiency and robustness of the resulting network connectivity. In the final work, we formulate the tour planning for a data mule to collect sensor data in underwater wireless sensor networks as an NP-hard energy-constrained biobjective underwater data muling problem (UDMP). UDMP has the two competing objectives of minimizing the length of a tour and maximizing the number of sensors contacted, while satisfying the energy constraint on the data mule at all times. We design three heuristic algorithms to address three UDMP cases of all-docking (UDMP-AD), dockingon- demand (UDMP-DoD), and non-confining-cover (UDMP-NCC), respectively. Each algorithm computes a set of Pareto-efficient solutions addressing the tradeoff between the two optimization objectives to facilitate tour planning. Simulation results validate the effectiveness and reveal the characteristics of each algorithm. Specifically, UDMPAD can often outperform the optimal solutions of a closely related NP-hard problem by using line-cover, UDMP-DoD usually produces shorter tours than UDMP-AD by avoiding unnecessary docking-visits, and UDMP-NCC can further shorten the tour length by choosing any geometrically computed positions as tour stops rather than confining tour stops to the exact locations of the given sensors.
Wireless Sensor Networks have a wide range of applications in different areas. Their main constraint is the limited and irreplaceable power source of the sensor nodes. In many applications, energy conservation of the sensor nodes and their replacement or replenishment due to the hostile nature of the environment is the most challenging issue. Energy efficient clustering and routing are the two main important topics studied extensively for this purpose. This book focuses on the energy efficient clustering and routing with a great emphasis on the evolutionary approaches. It provides a comprehensive and systematic introduction of the fundamentals of WSNs, major issues and effective solutions.
This book provides a comprehensive account of the glowworm swarm optimization (GSO) algorithm, including details of the underlying ideas, theoretical foundations, algorithm development, various applications, and MATLAB programs for the basic GSO algorithm. It also discusses several research problems at different levels of sophistication that can be attempted by interested researchers. The generality of the GSO algorithm is evident in its application to diverse problems ranging from optimization to robotics. Examples include computation of multiple optima, annual crop planning, cooperative exploration, distributed search, multiple source localization, contaminant boundary mapping, wireless sensor networks, clustering, knapsack, numerical integration, solving fixed point equations, solving systems of nonlinear equations, and engineering design optimization. The book is a valuable resource for researchers as well as graduate and undergraduate students in the area of swarm intelligence and computational intelligence and working on these topics.
Wireless Sensor Networks: Evolutionary Algorithms for Optimizing Performance provides an integrative overview of bio-inspired algorithms and their applications in the area of Wireless Sensor Networks (WSN). Along with the usage of the WSN, the number of risks and challenges occurs while deploying any WSN. Therefore, to defeat these challenges some of the bio-inspired algorithms are applied and discussed in this book. Discussion includes a broad, integrated perspective on various challenges and issues in WSN and also impact of bio-inspired algorithms on the lifetime of the WSN. It creates interdisciplinary theory, concepts, definitions, models and findings involved in WSN and Bio-inspired algorithms making it an essential guide and reference. It includes various WSN examples making the book accessible to a broader interdisciplinary readership. The book offers comprehensive coverage of the most essential topics, including: Evolutionary algorithms Swarm intelligence Hybrid algorithms Energy efficiency in WSN Load balancing of gateways Localization Clustering and routing Designing fitness functions according to the issues in WSN. The book explains about practices of shuffled complex evolution algorithm, shuffled frog leaping algorithm, particle swarm optimization and dolphin swarm optimization to defeat various challenges in WSN. The author elucidates how we must transform our thinking, illuminating the benefits and opportunities offered by bio-inspired approaches to innovation and learning in the area of WSN. This book serves as a reference book for scientific investigators who shows an interest in evolutionary computation and swarm intelligence as well as issues and challenges in WSN.
