WSN11-1: Distributed Cross-Layer Optimization of Wireless Sensor Networks: A Game Theoretic Approach

The layering principle has been long identified as a way to increase the interoperability and to improve the design of telecommunication protocols, where each layer offers services to adjacent upper layers and requires functionalities from adjacent lower ones. In the past, layering has enabled fast development of interoperable systems, but at the same time limited the performance of the overall architecture, due to the lack of coordination among layers. This issue is particularly relevant for wireless networks, where the very physical nature of the transmission medium introduces several performance limitations for protocols designed for wired networks. To overcome these limitations, a modification of the layering paradigm has been proposed, namely, cross-layer design, or “cross-layering.” Several cross-layering approaches have been proposed in the literature so far. Nevertheless, little formal characterization of the cross-layer interaction among different levels of the protocol stack is available yet. A clear need exists for identifying approaches able to analyze and provide quantitative guidelines for the design of cross-layer solutions, and, more importantly, to decide, in each case, whether cross-layering represents an effective solution or not. This chapter provides a detailed survey of the state-of-the-art and future directions in the usage of formal methods for cross-layer modeling and optimization of wireless networks. The text starts by detailing the principles of layered (ISO/OSI and TCP/IP) protocol stacks as well as the cross-layer paradigm. An overview of the architectures of existing and perspective wireless networks is presented along with an analysis of the potential limitations deriving from the layering approach and detailed description of possible optimization solutions enabled by cross-layer design. Subsequent sections are devoted to the issues of modeling and optimization of wireless networks. The remaining sections cover performance optimization as well as architecture optimization (specifically in terms of signaling). The chapter ends with a summary and outlines about future directions of research on the topic.

Developments in last decade in the field of electronics & communication, computer science and information technology field have resulted in the new technology paradigm, Wireless Sensor Networks. WSN is becoming increasingly popular due to their wide range of applications. For successful adaption of WSN in real field applications, there are certain issues which need to be addressed particularly the issue of energy consumption associated with sensor nodes. This limitation combined with node deployment has added many challenges to design and management of wireless sensor networks. The WSNs are involved in applications such as remote environment monitoring, event tracking in areas where providing electrical energy is very difficult. Moreover, the battery fed sensor nodes have very small energy associated with them, so preserving that energy and maximizing the usage of that energy is most critical issue in WSNs. Here, we present a survey of various energy optimization and lifetime enhancement techniques in wireless sensor networks and makes the reader aware about various energy saving techniques in WSN. They are analyzed from various points of view: Device hardware, Transmission, MAC protocols and Routing protocols.

Wireless Sensor Networks (WSNs) are consist of very tiny nodes structure, with very small batteries without having facility to recharge it. It works under any hard circumstances, hence this type of network is gaining high importance day by day. Maximum focus of researchers is to enhance or optimized its energy efficiency level. Routing protocol is the main energy expensive operation of sensor networks which utilize maximum energy resources of sensor network. Already a good number of routing protocols has been designed for energy efficiency or optimization with different approaches under controlled conditions. With this research we try to propose energy optimization of wireless sensor network through our proposed position response routing protocol (PRRP), under certain controlled parametric consideration. Research results shows a significant energy optimization in the life cycle of WSN. Index Terms—WSN, energy efficient, WSN routing protocol, GPS, PRRP, energy optimization

Wireless sensor networks consists of collection of autonomous sensors distributed at different geographical locations. Wireless sensor networks are mainly used to measure and monitor environmental and physical conditions such as sound, pressure, temperature, and lot more. Wireless Sensor networks has gain its popularity in recent years and is most widely used. The reason behind is popularity is that it can sense and monitor the physical and environmental conditions in the area where human reach is ruled out. These sensors sense the conditions and wirelessly transmits the data to the base station. The base station is nothing but a router which receives the data from various such wireless sensor networks. The various sensor networks which transmits the data to base station is called as nodes. When these nodes sends the data to the base station, an energy has been utilized by these nodes to transmit the data similarly energy is also utilized by the router to route the data to other nodes. By means of this paper, the energy consumed by the nodes and base station to transmit the data to each other has been optimized by using a combination of compression and encryption techniques on data to be transmitted.

The wireless sensor network (WSN) provides new paradigm for factory automation applications such as asset tracking, machine condition monitoring and distributed device control. Such new paradigm posed challenges for both control and communication. This paper proposes a design framework for industrial sensing and control applications on WSN platform. The proposed framework uses cross-layer design and optimization techniques to strike the balance between best estimation efforts and minimum cost of WSN resources. The novel optimization decomposition techniques and algorithms are proposed to resolve the cross-layer optimization problem. The demo application in a work-in-progress project shows the effectiveness of building wireless sensing and control applications using WSN with related optimization techniques.

Cross-layer optimization (physical, data link and network layers) in wireless networks has been limited by its combinatorial complexity (Toumpis and Goldsmith, 2003). Existing literature has traditionally been able to provide numerical analysis only for networks with less than 8 nodes (Madan et al., 2005), limiting our ability to understand the benefits of cross-layer optimization for more realistic-sized networks. In Barreto (2004) and Johansson and Xiao (2006) the authors showed that column generation could be used to reduce the complexity of the problem, with Johansson and Xiao (2006) providing in-depth analysis for 10-node networks and limited results for 20-node networks - all with minimum SNR and transmission rate restrictions. In this paper we show how decomposition methods can be used for the joint optimization of the physical (rate and power control), data link (MAC/scheduling), and networking (routing) layers, increasing the size of networks that can be analyzed. We present exact numerical solutions for ad-hoc networks with up to 30 nodes and mesh networks with up to 40 nodes, a significant scalability improvement. We continue by showing how bounded solutions for larger networks can be obtained with decreased computational complexity and present to heuristic implementations that increase the analysis to arbitrary-sized networks (Barreto and Chiu, 2006). We show how decomposition methods effectively decouple routing from the MAC/physical layers and allow us to solve the problems independently while reaching a global optimal solution.

Wireless sensor networks are the sensors which are acclimated to sense the environmental condition like temperature, pressure, sultriness, moisture etc, sensing the environment parameters and sending them to the gateway and retrieving the aggregated data from the gateway to the end user. Power is the major constraint in wireless sensor networks. One must need to reduce the power consumption. Wireless sensor networks have sensor nodes in which each node has a processor, antenna and a battery. The batteries consume power so that we require increasing the lifetime of the battery for that some optimization techniques are required to reduce the power consumption. There are some techniques which are inspired from the lifestyle of animals. In this paper proposing an optimization technique which is inspired by the animal demeanor which reduces the power consumption of the sensor nodes which is particle swarm optimization (PSO) technique. PSO is inspired by the convivial demeanor of birds or schooling of fish. By utilizing this bio-inspired technique we can reduce the power consumed by the sensor nodes and at the same time lifetime of the batteries present in the sensor nodes are increased.

Wireless sensor networks (WSNs) have shown to be an efficient solution for physical parameters monitoring and activity detection. WSNs are usually designed using single optimization by optimizing one objective function such as energy consumption taking some constraints into consideration. In multi-objective optimization (MOPT) multiple and often conflicting objectives need to be optimized while satisfying some constraints. This paper proposes MOPT-based routing and flow rate assignment methodologies for WSNs. The first methodology employs Karush-Kuhn-Tucker (KKT) conditions to find the optimal solution, while the second one is based on genetic algorithms (GA). A new formulation is introduced jointly minimize the energy and the delay. Performance metrics such as the average end-to-end delay and energy consumption are evaluated and compared with those of previous work, namely the hybrid geographical routing (HGR) algorithm. The proposed work helps network designers to address design procedures efficiently and increase the flexibility in the energy delay tradeoff for communications in WSNs.

Energy optimization and coverage area optimization of wireless sensor networks (WSN) are two major challenges to accomplish reliability optimization in the field of WSN. Reliability optimization in the field of WSN is directly connected to the performance and efficiency and consistency of the network. In this paper, the authors describe how these challenges can be resolved by designing an efficient WSN with the help of meta-heuristic algorithms. They have configured an optimized route/path using ant colony optimization (ACO) algorithm and deployed static WSN nodes. After configuring an efficient network, if we can maximize the coverage area, then we can ensure that the network is a reliable network. For coverage area optimization, they used a hybrid differential evolution-quantum behaved particle swarm optimization (DE-QPSO) algorithm. The result has been compared with existing literature, and the authors found good results applying those meta-heuristic and hybrid algorithms.

This paper is focused on the cross-layer design problem of joint multiuser detection and power control for energy-efficiency optimization in wireless data networks through a game-theoretic approach. Unlike previous studies in this area, which have considered the application of game theory to achieve energy-efficiency in idealized synchronous code division multiple access systems, more realistic assumptions including system asynchronism, the use of band-limited chip-pulses, and the multipath distortion induced by the wireless channel are explicitly incorporated in the development of this paper. Several noncooperative games are proposed wherein each user may vary its transmit power and uplink receiver in order to maximize its utility, which is defined here as the ratio of data throughput to transmit power. In particular, the case in which a linear multiuser detector is adopted at the receiver is considered first, and then, the more challenging case in which a nonlinear decision feedback multiuser detector is employed is considered. Via large system analysis (LSA), a decentralized implementation of the power allocation game requiring very little prior information on the interference background is proposed. LSA is also used to compare the energy efficiency of several linear multiuser detectors, and to obtain the optimal (i.e., utility-maximizing) length of the training sequence of each data frame. Numerical results show the effectiveness of the proposed solutions, as well as a very satisfactory agreement of the LSA-based analysis with simulation results obtained for systems with finite (and not so large) numbers of users.

Data processing and communication strategies for lifetime optimization in wireless sensor networks

SummaryWireless sensor networks will be at the epitome of applications in near future. It is going through tremendous positive changes. Although it suffers from some limitations and it sorts out its limitations day by day. The biggest limitation of wireless sensor networks is the limited energy of the nodes. Most of the energy is used during the routing of the data. An optimized way of routing will save the valuable energy of the node and helpful for increasing the network's lifetime. The optimization algorithms are indeed utterly helpful in many applications across the different fields to optimize the resources. In the proposed worked we have applied Adaptive Particle Swarm Optimization (APSO), Ant Colony Optimization (ACO), Genetic Algorithms (GA), and Simulated Annealing (SA) to a Modified Rendezvous Point Selection Scheme and observed the effect on the network lifetime and energy consumption in the network. We have made an exhaustive comparison of all the optimization algorithms with considerable simulations.

Wireless sensor networks (WSNs) have recently acquired prominence in a variety of applications such as remote monitoring and tracking. Since it is virtually hard to recharge the nodes in their remote deployment, also, the transmission of data from nodes to the base station requires a significant amount of energy. Thus, our research proposes a routing protocol, namely hybrid falcon-improved ACO Nature-Inspired Optimization using a deep learning model to reduce energy consumption while increases the network lifetime. In the developed model, initially, the falcon optimization technique is utilized to locate the best possible cluster heads in the quickest possible time. Furthermore, to improve the quality of service in routing optimization a new improved ACO has been proposed in which linear flexible operator and the premier operator are used to increasing the iteration speed. Finally, the optimum route is obtained through DBNN based on predicted energy. As a result, our proposed model gives a lifetime as 121 s and energy consumption as 0.041 J at 500 rounds when compared to the baseline approaches. Therefore, our proposed approaches provides better routing and improves the QoS as well as the energy consumption which increases the longevity of mobile nodes.

Towards energy balancing optimization in wireless sensor networks: A novel quantum inspired genetic algorithm based sinks deployment approach

A coverage optimization method is suggested to improved node coverage in wireless sensor networks (WSNs), decrease node redundancy, and lengthen network lifetime, this method is based on the adaptive grey wolf arithmetic optimization algorithm The exploitation and exploration are first balanced using a cosine acceleration function. Then, to boost the method's performance, the grey wolf optimizer and arithmetic optimization algorithm are combined. In comparison to the conventional coverage optimization algorithm, our simulation results could improve arithmetic optimization algorithm has higher convergence efficiency and coverage rate, decreases network loss, and significantly lengthens the network life cycle.