Network Lifetime Optimization by KKT Optimality Conditions in Wireless Sensor Networks

Energy saving is a critical issue for typical wireless sensor networks (WSNs) and the energy consumption is a big challenge to the design of WSNs. In this paper, we investigate this problem by a cross-layer design approach to minimize energy consumption and maximize network lifetime (NL) for a multiple-source and single-sink (MSSS) WSN with energy constraints. The optimization problem for the MSSS WSNs can be formulated as a mixed integer convex optimization problem with adoption of time division multiple access (TDMA) at medium access control (MAC) layer and it becomes a convex problem by relaxing an integer constraint on time slots. First of all, we have employed the Karush-Kuhn-Tucker(KKT) optimality conditions to derive analytical expressions of the globally optimal NL for a linear SSSS topology. Then a decomposition and combination (D&C) approach has been proposed to obtain suboptimal solutions. As a result, an analytical expression of the suboptimal NL has been derived for WSNs with a linear MSSS topology. To validate the analysis, numerical results show that the upper-bounds of the NL obtained by our proposed optimization models are tight. Important insights into the NL and benefits of cross-layer design for WSN NLM are also summarized.

Sandpiper optimization algorithm with cosine similarity based cross-layer routing protocol for smart agriculture in wireless sensor network assisted internet of things systems

The Internet of Things (IoT) has recently attained a prominent role in enabling smooth and effective communication among various networks. Wireless sensor network (WSN) is utilized in IoT to collect peculiar data without interacting with humans in specific applications. Energy is a major problem in WSN-assisted IoT applications, even though better data communication is achieved through cross-layer models. This paper proposes a new cross-layer-based clustering and routing model to provide a scalable and energy-efficient long data communication in WSN-assisted IoT systems for smart agriculture. Initially, the fuzzy k-medoids clustering approach is used to split the network into various clusters since the formation of clusters plays an important role in energy consumption. Then, a new swarm optimization known as enhanced sparrow search algorithm (ESSA), which is the combination of SSA and chameleon swarm algorithm (CSA), has been introduced for optimal cluster head (CH) selection to solve the energy-hole problems in WSN. A cross-layer strategy has been preferred to provide efficient data transmission. Each sensor node parameter of the physical layer, network layer and medium access control (MAC) is considered for processing routing. Finally, a new bio-inspired algorithm is known as the sandpiper optimization algorithm (SOA), and cosine similarity (CS) has been employed to determine the optimal route for efficient data transmission and retransmission. The simulation of the proposed protocol is implemented by network simulator (NS2), and the simulation results are taken in terms of end-to-end delay, PDR, communication overhead, communication cost, average consumed energy, and network lifetime.     

A Sensor Network Cross-Layer Power Control Algorithm that Incorporates Multiple-Access Interference

This paper presents a wireless sensor network (WSN) transmit power control algorithm designed to minimize WSN node energy consumption. The algorithm determines transmit power levels using an optimization that accounts for energy consumed by the physical and link layers of the protocol stack. This cross-layer optimization incorporates a physical layer model that uses knowledge of the WSN medium access control (MAC) layer algorithm to accurately model multiple access interference (MAI). Analytical and simulation results show that accounting for MAI in this fashion results in a significant energy savings relative to comparable WSN power control algorithms.     

Cross-layer design and analysis of WSN-based mobile target detection systems

The limited node capabilities typical of Wireless Sensor Networks (WSNs) call for cross-layer design optimization. In this paper, we address the problem of designing and operating long-lasting surveillance mobile target detection applications for unattended WSNs with a priori knowledge of the nodes’ positions. In particular, we focus on the cross-layer interaction between the sensing layer (devoted to the detection of a mobile target crossing the monitored area) and the communication layer (devoted to the transmission of the alert, upon detection, from a sensing node to the network sink). The performance of the sensing layer is characterized by the probability of target missed detection and the delay before the first sensor detection act. The communication layer is investigated considering two Medium Access Control (MAC) protocols: X-MAC [1] and the novel Cascade (Cas)-MAC protocol, inspired by the principles of the D-MAC protocol [2]. At both layers, we validate analytical models through realistic simulations and experiments. The cross-layer interaction between the two layers is achieved considering a proper model for the network lifetime, based on the average energy depletion at the node level. Finally, to highlight the benefits of the proposed framework, we present a cross-layer optimization approach for the configuration of the system parameters, considering several relevant network topologies.     

Optimized mobile sink based grid coverage-aware sensor deployment and link quality based routing in wireless sensor networks

Mobile sink (MS) has been used in wireless sensor networks (WSN) to increase the network lifetime by changing the location over time. The major quality of service given by WSN is coverage energy consumption (EC) and network lifetime. There are many methods implemented for enhance the coverage hole restoration and reduce the EC. We propose a novel MSCOLER (MS based Coverage Optimization and Link-stability Estimation Routing) protocol for Optimal Coverage restoration and Link stability Estimation. An optimization algorithm is used to optimize the coverage hole and move the redundant node besides the hole. During the routing process, link quality based routing is used to discover the relay nodes with the estimation of link stability to enhance the entire network lifetime and practically make the perfect transmission distance for energy saving. Experimental results demonstrate that proposed protocol can solve the coverage restoration problem, decrease the EC and reduce the network lifetime. The performance is evaluated regarding Average of residual energy (ARE), Receiving packets ratio (RPR), Moving energy consumption (MEC), Network lifetime (NL), Percentage of coverage (%C) and Average Energy Consumption (AEC).     

Joint optimization of energy allocation and routing problems in wireless sensor networks

Energy allocation problems and routing problems are both important research issues in the wireless sensor network (WSN) field. The former usually aims at considering how to allocate a certain number of sensor devices in a sensing region to form a WSN so that the objective function value (e.g., the network connectivity or the network lifetime) of the constructed network is optimized. For the message routing problem in WSNs, researchers tend to consider how to find an energy conservable message transmission routing scheme for notifying the supervisor of the WSN when an event occurs. Till now, many solutions have been proposed for the above two categories of optimization problems. However, unifying the above two network optimization problems to maximize the network lifetime, to the best of our knowledge, still lacks related research. This paper considers a joint optimization problem for energy allocation and energy‐aware routing called the joint optimization of energy allocation and routing problem (JOEARP) for a hierarchical cluster‐based WSN. We propose an exact algorithm to provide the optimum solution for the JOEARP. The simulation results show that this solution performed better in prolonging the network lifetime of a WSN in a real situation, compared to other compositions of conventional energy allocation schemes with some known routing algorithms. Copyright © 2009 John Wiley & Sons, Ltd.     

Cross-layer network planning for multi-radio multi-channel cognitive wireless networks

We propose a general network planning framework for multi-radio multi-channel wireless networks. Under this framework, data routing, resource allocation, and scheduling are jointly designed to maximize a network utility function. We first treat such a cross-layer design problem with fixed radio distributions across the nodes and formulate it as a large-scale convex optimization problem. A primal-dual method together with the column-generation technique is proposed to efficiently solve this problem. We then consider the radio allocation problem, i.e., the optimal placement of radios within the network to maximize the network utility function. This problem is formulated as a large- scale combinatorial optimization problem. We derive the necessary conditions that the optimal solution should satisfy, and then develop a sequential optimization scheme to solve this problem. Simulation studies are carried out to assess the performance of the proposed cross-layer network planning framework. It is seen that the proposed approach can significantly enhance the overall network performance.     

Minimizing Power Cost in QoS Constrained Wireless Sensor Networks

This paper studies the problem of simultaneous support of energy efficiency and quality of service (QoS) in time division multiple access (TDMA) based wireless sensor networks (WSNs), utilizing the concept of cross-layer optimization. We show that by taking slot reuse into account, the problem has combinatorial complexity, which is determined by the TDMA frame length and the number of links. An iterative reuse factor (IRF) approach is proposed to efficiently solve the problem. By skillfully introducing and iteratively adjusting a slot reuse factor, cross-layer optimization and slot reuse are jointly inter-connected to obtain a TDMA schedule with optimal power consumption and desired QoS objectives in polynomial time. Extensive simulations with a random topology WSN show that the proposed IRF approach provides a flexible tradeoff between energy efficiency and QoS objectives. Under the same packet loss rate objective, the IRF approach achieves up to 30 % of power saving compared to the existing approaches in the literature.     

Cross-Layer Design for Lifetime Maximization in Interference-Limited Wireless Sensor Networks

We consider the joint optimal design of the physical, medium access control (MAC), and routing layers to maximize the lifetime of energy-constrained wireless sensor networks. The problem of computing lifetime-optimal routing flow, link schedule, and link transmission powers for all active time slots is formulated as a non-linear optimization problem. We first restrict the link schedules to the class of interference-free time division multiple access (TDMA) schedules. In this special case, we formulate the optimization problem as a mixed integerconvex program, which can be solved using standard techniques. Moreover, when the slots lengths are variable, the optimization problem is convex and can be solved efficiently and exactly using interior point methods. For general non-orthogonal link schedules, we propose an iterative algorithm that alternates between adaptive link scheduling and computation of optimal link rates and transmission powers for a fixed link schedule. The performance of this algorithm is compared to other design approaches for several network topologies. The results illustrate the advantages of load balancing, multihop routing, frequency reuse, and interference mitigation in increasing the lifetime of energy-constrained networks. We also briefly discuss computational approaches to extend this algorithm to large networks     

Enhanced performance based on cross-layer design from physical to transport layers for multihop wireless networks

This paper puts forward a novel cognitive cross-layer design algorithms for multihop wireless networks optimization across physical,mediam access control(MAC),network and transport layers.As is well known,the conventional layered-protocol architecture can not provide optimal performance for wireless networks,and cross-layer design is becoming increasingly important for improving the performance of wireless networks.In this study,we formulate a specific network utility maximization(NUM)problem that we believe is appropriate for multihop wireless networks.By using the dual algorithm,the NUM problem has been optimal decomposed and solved with a novel distributed cross-layer design algorithm from physical to transport layers.Our solution enjoys the benefits of cross-layer optimization while maintaining the simplicity and modularity of the traditional layered architecture.The proposed cross-layer design can guarantee the end-to-end goals of data flows while fully utilizing network resources.Computer simulations have evaluated an enhanced performance of the proposed algorithm at both average source rate and network throughput.Meanwhile,the proposed algorithm has low implementation complexity for practical reality.     

Lifetime Maximization Based on Coverage and Connectivity in Wireless Sensor Networks

We consider information retrieval in a wireless sensor network deployed to monitor a spatially correlated random field. We address optimal sensor scheduling and information routing under the performance measure of network lifetime. Both single-hop and multi-hop transmissions from sensors to an access point are considered. For both cases, we formulate the problems as integer programming based on the theories of coverage and connectivity in sensor networks. We derive upper bounds for the network lifetime that provide performance benchmarks for suboptimal solutions. Suboptimal sensor scheduling and data routing algorithms are proposed to approach the lifetime upper bounds with reduced complexity. In the proposed algorithms, we consider the impact of both the network geometry and the energy consumption in communications and relaying on the network lifetime. Simulation examples are used to demonstrate the performance of the proposed algorithms as compared to the lifetime upper bounds.     

Efficient REBTA data reporting algorithm for object tracking in wireless sensor networks

Object tracking is widely referred as one of the most interesting applications of wireless sensor networks (WSNs). This application is able to detect and track objects and report information about these objects to a central base station. One of the major drawbacks in the current research in WSNs is the quality of the data reporting where the major research focus is dedicated to localization of objects; however, few of these works were concentrated on the data reporting. An efficient data reporting algorithm for object tracking in WSNs is proposed in this paper. The main objective of this paper is to enhance the WSN lifetime by achieving both minimum energy and balancing such consumption in sensor nodes during reporting operation. Furthermore, in our model, the enhancement of network reliability is considered. Finally, it reduces the effects of congestion by sufficiently utilizing the under loaded nodes to improve the network throughput. This paper formulates the object tracking problem in large‐scale WSN into 0/1 integer linear programming problem, and then proposes a reliable energy balance traffic aware approach to solve the optimization problem. From the obtained simulation results, the proposed solution has proved to be able to enhance the network performance in network lifetime, throughput, end‐to‐end delay, energy balance, and complexity for both homogeneous and heterogeneous networks.     

A New Systematic Framework for Autonomous Cross-Layer Optimization

Cross-layer optimization solutions have been proposed in recent years to improve the performance of wireless users that operate in a time-varying, error-prone network environment. However, these solutions often rely on centralized cross-layer optimization solutions that violate the layered network architecture of the protocol stack by requiring layers to provide access to their internal protocol parameters to other layers. This paper presents a new systematic framework for cross-layer optimization, which allows each layer to make autonomous decisions to maximize the wireless user's utility by optimally determining what information should be exchanged among layers. Hence, this cross-layer framework preserves the current layered network architecture. Since the user interacts with the wireless environment at various layers of the protocol stack, the cross-layer optimization problem is solved in a layered fashion such that each layer adapts its own protocol parameters and exchanges information (messages) with other layers that cooperatively maximize the performance of the wireless user. Based on the proposed layered framework, we also design a message-exchange mechanism that determines the optimal cross-layer transmission strategies, given the user's experienced environment dynamics.     

RIS辅助共生无线电通信的速率分拆多址资源分配方案

随着通信场景和网络架构的日益复杂,无线电网络频谱资源稀缺与能耗问题是无线通信的关键挑战,为此,提出了一种新的基于速率分拆多址接入技术（Rate Splitting Multiple Access,RSMA）广播信号的可重构智能表面（Reconfigurable Intelligent Surface,RIS）辅助共生无线电（Symbiotic Radio,SR）系统方案。在该方案中主发射机采用RSMA的方式广播信号,并将传统高功耗次发射机用低功耗RIS替代协作信号的后向散射传输。在主发射机发射波束形成,RIS相移系数和公有信息速率分配约束下,构建了最大化主接收最小速率的问题。由于问题的非凸性和变量的耦合性,提出了一种基于逐次凸逼近,凸差函数,罚函数的交替优化方法以求得次优解。仿真结果表明,与空分多址接入和非正交多址接入方案相比,所提RSMA方案能显著提升用户速率。     

Design of Translucent Optical Networks: Partitioning and Restoration

We discuss the problem of designing translucent optical networks composed of restorable, transparent subnetworks interconnected via transponders. We develop an integer linear programming (ILP) formulation for partitioning an optical network topology into subnetworks, where the subnetworks are determined subject to the constraints that each subnetwork satisfies size limitations, and it is two-connected. A greedy heuristic partitioning algorithm is proposed for planar network topologies. We use section restoration for translucent networks where failed connections are rerouted within the subnetwork which contains the failed link. The network design problem of determining working and restoration capacities with section restoration is formulated as an ILP problem. Numerical results show that fiber costs with section restoration are close to those with path restoration for mesh topologies used in this study. It is also shown that the number of transponders with the translucent network architecture is substantially reduced compared to opaque networks.     

Modeling and Optimization of Transmission Schemes in Energy-Constrained Wireless Sensor Networks

We consider a wireless sensor network with energy constraints. We model the energy consumption in the transmitter circuit along with that for data transmission. We model the bottom three layers of the traditional networking stack - the link layer, the medium access control (MAC) layer, and the routing layer. Using these models, we consider the optimization of transmission schemes to maximize the network lifetime. We first consider the optimization of a single layer at a time, while keeping the other layers fixed. We make certain simplifying assumptions to decouple the layers and formulate optimization problems to compute a strategy that maximizes the network lifetime. We then extend this approach to cross-layer optimization of time division multiple access (TDMA) wireless sensor networks. In this case, we construct optimization problems to compute the optimal transmission schemes to an arbitrary degree of accuracy and efficiently. We then consider networks with interference, and propose methods to compute approximate solutions to the resulting optimization problems. We give numerical examples that illustrate the computational approaches as well as the benefits of cross-layer design in wireless sensor networks.     

A boolean spider monkey optimization based energy efficient clustering approach for WSNs

Wireless sensor network (WSN) consists of densely distributed nodes that are deployed to observe and react to events within the sensor field. In WSNs, energy management and network lifetime optimization are major issues in the designing of cluster-based routing protocols. Clustering is an efficient data gathering technique that effectively reduces the energy consumption by organizing nodes into groups. However, in clustering protocols, cluster heads (CHs) bear additional load for coordinating various activities within the cluster. Improper selection of CHs causes increased energy consumption and also degrades the performance of WSN. Therefore, proper CH selection and their load balancing using efficient routing protocol is a critical aspect for long run operation of WSN. Clustering a network with proper load balancing is an NP-hard problem. To solve such problems having vast search area, optimization algorithm is the preeminent possible solution. Spider monkey optimization (SMO) is a relatively new nature inspired evolutionary algorithm based on the foraging behaviour of spider monkeys. It has proved its worth for benchmark functions optimization and antenna design problems. In this paper, SMO based threshold-sensitive energy-efficient clustering protocol is proposed to prolong network lifetime with an intend to extend the stability period of the network. Dual-hop communication between CHs and BS is utilized to achieve load balancing of distant CHs and energy minimization. The results demonstrate that the proposed protocol significantly outperforms existing protocols in terms of energy consumption, system lifetime and stability period.     

基于遗传聚类的无线传感器负载均衡路由算法

通常的无线传感器分簇网络存在节点负载不均衡的问题。为均衡各节点能量消耗,延长网络生存周期,将K均值算法与遗传算法相结合,提出一种负载均衡的无线传感器网络路由算法,算法利用遗传算法的全局寻优能力以克服传统K均值算法的局部性和对初始中心的敏感性,实现了传感器网络节点自适应成簇与各节点负载均衡。仿真实验表明,该算法显著延长了网络寿命,相对于其他分簇路由算法,其网络生存时间延长了约43%。     

Cross-layer optimization for SVC video delivery over the IEEE 802.11e wireless networks

The Scalable Video Coding (SVC) standard extends the H.264/AVC with scalability support and is effective to adapt bitrate to the time-varying wireless channel bandwidth. In this paper, we propose a cross-layer optimization scheme, which includes packet prioritization and QoS mapping, for the delivery of SVC over the IEEE 802.11e wireless networks. The proposed structure enables interaction among different network layers, providing differentiated services for video packets. Our cross-layer optimization performs with the following information: (i) SVC packet prioritization at the application layer, (ii) service differentiation at the MAC layer, and (iii) interface queue (IFQ) occupation status at the link layer. We formulate the QoS mapping problem as a joint optimization of access category (AC) assignment and IFQ control. A novel and efficient solution is proposed to reduce the computational complexity of the joint optimization problem. Simulation results show that the proposed approach achieves notable improvement when compared to conventional methods.     

Multi-Objective Evolutionary Algorithm Based on Decomposition for Energy Efficient Coverage in Wireless Sensor Networks

Wireless sensor networks (WSNs) have become a hot area of research in recent years due to the realization of their ability in myriad applications including military surveillance, facility monitoring, target detection, and health care applications. However, many WSN design problems involve tradeoffs between multiple conflicting optimization objectives such as coverage preservation and energy conservation. Many of the existing sensor network design approaches, however, generally focus on a single optimization objective. For example, while both energy conservation in a cluster-based WSNs and coverage-maintenance protocols have been extensively studied in the past, these have not been integrated in a multi-objective optimization manner. This paper employs a recently developed multi-objective optimization algorithm, the so-called multi-objective evolutionary algorithm based on decomposition (MOEA/D) to solve simultaneously the coverage preservation and energy conservation design problems in cluster-based WSNs. The performance of the proposed approach, in terms of coverage and network lifetime is compared with a state-of-the-art evolutionary approach called NSGA II. Under the same environments, simulation results on different network topologies reveal that MOEA/D provides a feasible approach for extending the network lifetime while preserving more coverage area.