Optimal base-station locations in two-tiered wireless sensor networks

We consider generic two-tiered wireless sensor networks (WSNs) consisting of sensor clusters deployed around strategic locations, and base-stations (BSs) whose locations are relatively flexible. Within a sensor cluster, there are many small sensor nodes (SNs) that capture, encode, and transmit relevant information from a designated area, and there is at least one application node (AN) that receives raw data from these SNs, creates a comprehensive local-view, and forwards the composite bit-stream toward a BS. This paper focuses on the topology control process for ANs and BSs, which constitute the upper tier of two-tiered WSNs. Since heterogeneous ANs are battery-powered and energy-constrained, their node lifetime directly affects the network lifetime of WSNs. By proposing algorithmic approaches to locate BSs optimally, we can maximize the topological network lifetime of WSNs deterministically, even when the initial energy provisioning for ANs is no longer always proportional to their average bit-stream rate. The obtained optimal BS locations are under different lifetime definitions according to the mission criticality of WSNs. By studying intrinsic properties of WSNs, we establish the upper and lower bounds of maximal topological lifetime, which enable a quick assessment of energy provisioning feasibility and topology control necessity. Numerical results are given to demonstrate the efficacy and optimality of the proposed topology control approaches designed for maximizing network lifetime of WSNs.     

Energy analysis using semi-Markov modeling for the base station in 5G networks

With the increasing prevalence of wireless sensor networks, the uninterrupted operation of these networks becomes essential. To ensure continuous functionality, wireless networks rely on available base stations (BSs). However, the persistent operation of BSs comes at the cost of substantial energy consumption. Consequently, given the surge in wireless network traffic and the necessity for uninterrupted BS availability, energy efficiency within the BS becomes a concerning issue. To address this, the study employs a semi-Markov model to depict the availability of the BS, with states corresponding to the failures of its components (baseband unit, remote unit, and software module). The analysis yields a steady-state solution, with reward rates assigned to each state based on the energy consumption of individual BS components. This approach enables the determination of the expected energy consumption within this model. Additionally, the BS's throughput is assessed using an M/G/1 queueing model with server breakdown.     

Load-balance cell association for improving network capacity in heterogeneous cellular networks

Heterogeneous cellular networks improve the spectrum efficiency and coverage of wireless communication networks by deploying low power base station （BS） overlapping the conventional macro cell. But due to the disparity between the transmit powers of the macro BS and the low power BS, cell association strategy developed for the conventional homogeneous networks may lead to a highly unbalanced traffic loading with most of the traffic concentrated on the macro BS. In this paper, we propose a load-balance cell association scheme for heterogeneous cellular network aiming to maximize the network capacity. By relaxing the association constraints, we can get the upper bound of optimal solution and convert the primal problem into a convex optimization problem. Furthermore we propose a Lagrange multipliers based distributed algorithm by using Lagrange dual theory to solve the convex optimization, which converges to an optimal solution with a theoretical performance guarantee. With the proposed algorithm, mobile terminals （MTs） need to jointly consider their traffic type, received signal-to-interference-noise-ratios （SINRs） from BSs, and the load of BSs when they choose server BS. Simulation results show that the load balance between macro and pico BS is achieved and network capacity is improved significantly by our proposed cell association algorithm.     

Variable Bit Rate Flow Routing in Wireless Sensor Networks

Since energy constraint is a fundamental issue for wireless sensor networks, network lifetime performance has become a key performance metric for such networks. In this paper, we consider a two-tier wireless sensor network and focus on the flow routing problem for the upper tier aggregation and forwarding nodes (AFNs). Specifically, we are interested in how to perform flow routing among the nodes when the bit rate from each source node is time-varying. We present an algorithm that can be used to construct a flow routing solution with the following properties: (1) If the average rate from each source node is known a priori, then flow routing solution obtained via such algorithm is optimal and offers provably maximum network lifetime performance; (2) If the average rate of each source node is unknown but is within a fraction (epsiv) of an estimated rate value, then network lifetime by the proposed flow routing solution is within 2epsiv/1-epsiv from the optimum. These results fill in an important gap in theoretical foundation for flow routing in energy-constrained sensor networks.     

Multi-Source Temporal Data Aggregation in Wireless Sensor Networks

Data aggregation has been emerged as a basic approach in wireless sensor networks (WSNs) in order to reduce the number of transmissions of sensor nodes.This paper proposes an energy-efficient multi-source temporal data aggregation model called MSTDA in WSNs. In MSTDA model, a feature selection algorithm using particle swarm optimization (PSO) is presented to simplify the historical data source firstly. And then a data prediction algorithm based on improved BP neural network with PSO (PSO-BPNN) is proposed. This MSTDA model, which helps to find out potential laws according to historical data sets, is deployed at both the base station (BS) and the node. Only when the deviation between the actual and the predicted value at the node exceeds a certain threshold, the sampling value and new model are sent to BS. The experiments on the dataset which comes from the actual data collected from 54 sensors deployed in the Intel Berkeley Research lab made a satisfied performance. When the error threshold greater than 0.15, it can decrease more than 80% data transmissions.     

Assimilation of fuzzy clustering approach and EHO‐Greedy algorithm for efficient routing in WSN

The problems related to energy consumption and improvement of the network lifetime of WSN (wireless sensor network) have been considered. The base station (BS) location is the main concern in WSN. BSs are fixed, yet, they have the ability to move in some situations to collect the information from sensor nodes (SNs). Recently, introducing mobile sinks to WSNs has been proved to be an efficient way to extend the lifespan of the network. This paper proposes the assimilation of the fuzzy clustering approach and the Elephant Herding Optimization (EHO)‐Greedy algorithm for efficient routing in WSN. This work considers the separate sink nodes of a fixed sink and movable sink to decrease the utilization of energy. A fixed node is deployed randomly across the network, and the movable sink node moves to different locations across the network for collecting the data. Initially, the number of nodes is formed into the multiple clusters using the enhanced expectation maximization algorithm. After that, the cluster head (CH) selection done through a fuzzy approach by taking the account of three factors of residual energy, node centrality, and neighborhood overlap. A suitable collection of CH can extremely reduce the utilization of energy and also enhancing the lifespan. Finally, the routing protocol of the hybrid EHO‐Greedy algorithm is used for efficient data transmission. Simulation results display that the proposed technique is better to other existing approaches in regard to energy utilization and the system lifetime.     

Offloading in Software Defined Network at Edge with Information Asymmetry: A Contract Theoretical Approach

The proliferation of highly capable mobile devices such as smartphones and tablets has significantly increased the demand for wireless access. Software defined network (SDN) at edge is viewed as one promising technology to simplify the traffic offloading process for current wireless networks. In this paper, we investigate the incentive problem in SDN-at-edge of how to motivate a third party access points (APs) such as WiFi and smallcells to offload traffic for the central base stations (BSs). The APs will only admit the traffic from the BS under the precondition that their own traffic demand is satisfied. Under the information asymmetry that the APs know more about own traffic demands, the BS needs to distribute the payment in accordance with the APs’ idle capacity to maintain a compatible incentive. First, we apply a contract-theoretic approach to model and analyze the service trading between the BS and APs. Furthermore, other two incentive mechanisms: optimal discrimination contract and linear pricing contract are introduced to serve as the comparisons of the anti adverse selection contract. Finally, the simulation results show that the contract can effectively incentivize APs’ participation and offload the cellular network traffic. Furthermore, the anti adverse selection contract achieves the optimal outcome under the information asymmetry scenario.     

A new handoff management system for heterogeneous wireless access networks

In heterogeneous wireless access networks, each mobile terminal may frequently need to change its base station (BS); this change is called a ‘handoff’. BSs have static parameters, which are related to their radio access technologies (RATs); however, they also contain dynamic parameters such as load and signal quality. Therefore, the problem of handoff decision includes two subproblems of RAT selection and BS selection. In this paper, first a ‘heterogeneous handoff management system’ for gathering all different required parameters is proposed. Second, a RAT Selection algorithm based on analytic hierarchy process and a BS Selection algorithm based on data envelopment analysis are designed. Finally, by means of ‘weight restriction’ technique, we develop a method for studying the impact of RAT Selection parameters on the performance of the network. Simulation results indicate that RAT Selection parameters have significant impact on the bandwidth utilization, energy consumption and the whole satisfaction of the users in heterogeneous wireless access networks.Copyright © 2012 John Wiley & Sons, Ltd.     

Small‐cell planning in LTE HetNet to improve energy efficiency

Cell planning is one essential operation in wireless networks, and it significantly affects system performance and cost. Many research efforts consider the cell planning problem with identical base stations (BSs) or to construct a new network on the region without any infrastructure. However, long‐term evolution (LTE) adopts heterogeneous network, which allows operators to tactically deploy small cells to enhance signal coverage and improve performance. It thus motivates us to propose a small‐cell planning problem by adaptively adding low‐powered BSs with the limitation of budget to an existing network to increase its energy efficiency, which is defined by the ratio of network throughput to the amount of energy consumption of BSs. We consider 2 types of LTE small cells, namely, microcells and picocells, and develop different clustering strategies to deploy these cells. Based on the available resource and traffic demand in each cell, we then adjust the transmitted power of the deployed BS with energy concern. Experimental results demonstrate that our small‐cell planning solution can achieve high‐energy efficiency of LTE networks, which means that BSs can better use their transmitted energy to satisfy the traffic demands of user devices. This paper contributes in proposing a practical problem for cell planning with heterogeneous network consideration and developing an efficient solution to provide green communications.     

Improved TOA‐Based Localization Method with BS Selection Scheme for Wireless Sensor Networks

The purpose of a localization system is to estimate the coordinates of the geographic location of a mobile device. The accuracy of wireless localization is influenced by non‐line‐of‐sight (NLOS) errors in wireless sensor networks. In this paper, we present an improved time of arrival (TOA)–based localization method for wireless sensor networks. TOA‐based localization estimates the geographic location of a mobile device using the distances between a mobile station (MS) and three or more base stations (BSs). However, each of the NLOS errors along a distance measured from an MS (device) to a BS (device) is different because of dissimilar obstacles in the direct signal path between the two devices. To accurately estimate the geographic location of a mobile device in TOA‐based localization, we propose an optimized localization method with a BS selection scheme that selects three measured distances that contain a relatively small number of NLOS errors, in this paper. Performance evaluations are presented, and the experimental results are validated through comparisons of various localization methods with the proposed method.     

Network energy optimization and intelligent routing in WSN applicable for IoT using self-adaptive coyote optimization algorithm

The Internet of Things (IoT) is a recent wireless telecommunications platform, which contains a set of sensor nodes linked by wireless sensor networks (WSNs). These approaches split the sensor nodes into clusters, in which each cluster consists of an exclusive cluster head (CH) node. The major scope of this task is to introduce a novel CH selection in WSN applicable to IoT using the self-adaptive meta-heuristic algorithm. This paper aids in providing the optimal routing in the network based on direct node (DN) selection, CH selection, and clone cluster head (CCH) selection. DNs are located near the base station, and it is chosen to avoid the load of CH. The adoption of the novel self-adaptive coyote optimization algorithm (SA-COA) is used for the DN selection and CCH selection. When the nodes are assigned in the network, DN and CCH selection is performed by the proposed SA-COA. Then, the computation of residual energy helps to select the CH, by correlating with the threshold energy. CCH is proposed to copy the data from the CH to avoid the loss of data in transmitting. By forming the CCH, the next CH can be easily elected with the optimal CCH using SA-COA. From the simulation findings, the best value of the designed SA-COA-LEACH model is secured at 1.14%, 3.17%, 1.18%, and 7.33% progressed than self-adaptive whale optimization algorithm (SAWOA), cyclic rider optimization algorithm (C-ROA), krill herd algorithm (KHA), and COA while taking several nodes 50. The proposed routing of sensor networks specifies better performance than the existing methods.     

一种适用于异步无线传感器网络的机会路由机制

无线传感器网络通常使用低占空比的异步睡眠调度来降低节点能耗。由于发送节点在接收节点醒来后才能向其发送数据,这将引入额外的等待时延。在最近的一些任播路由机制中,发送节点动态地选择最先醒来的候选节点转发数据,以最小化等待时延。但是,由于从最先醒来的候选节点到基站的时延可能并不低,任播路由机制并不一定能最小化端到端总时延。为此,提出了一种适用于异步无线传感器网络的机会路由机制,将路由决策建模为强马尔科夫过程,并根据最优停止理论推导出该过程一种简化的停止规则。仿真结果表明,节点到基站的最大端到端时延仅为基于地理位置的机会路由的68.5%。     

物联网感知层多路径传输策略研究

多路径数据传输是无线传感器网络亟需解决的一个关键问题.本文针对节点故障、链路失效和外界干扰影响网络稳定性和可靠性,提出一种基于混合蛙跳算法的无线传感器网络多路径传输策略.首先我们详细介绍了蛙跳算法及其原理,之后我们将其应用到无线传感器网络多路径传输策略之中,接着运用混合蛙跳算法对传感网络节点其进行更新、划分、重组以便选择出最优节点建立传输最优路径,提高网络的稳定性和可靠性.通过算法仿真与结果对比提出的算法与AODV、粒子群PSO算法相比,在网络能耗、传输时延、丢包率、连通率和可靠度等方面都具有较好的性能.其中网络能耗比AODV、PSO算法降低了62.5％和35.8％.     

基于节点信任值的安全层次路由协议

由于无线传感器网络容易受到攻击,所以保证无线传感器在网络数据传输过程中的路由安全是必要的,文中提出一种基于节点信任值、节点度和距离的簇头选举算法,进行路由主干节点的可信选举,建立安全可信的层次路由。仿真结果表明,该算法可有效评估节点的信任值,解决了节点失效或被俘获所导致的层次路由安全问题。     

Design and operation of energy efficient heterogeneous mobile networks

During the last two decades we have witnessed a rapid growth in wireless communication services, which has dramatically increased the number of necessary radio access components to provide the adequate capacity and acceptable quality of service. The power cost for operating this huge number of radio Base Stations (BSs) is under serious consideration by the mobile communication industry and recent research efforts have focused on energy efficient design and optimization of the radio access network in order to alleviate the energy consumption and mobile network operators OPEX. In this work, we consider a deployment of heterogeneous BSs to serve a given geographical area and propose two energy-aware algorithms to optimally determine the operational mode of those BSs under various traffic load conditions. Performance evaluation results show that the proposed algorithms can provide near optimal solutions and achieve substantial network energy consumption reduction without compromising the efficient operation of the mobile network. We further benefit from the outcome of this formulation framework and propose BS activation schemes that yield proper BS activation profiles for continuous-time operation in the same network deployment. Various traffic loads are investigated in several simulation campaigns and our proposed schemes yield quite satisfactory energy saving gains compared to fully operational topology networks in all scenarios of interest examined.     

Wi-Fi offloading between LTE and WLAN with combined UE and BS information

With the increasing popularity of mobile devices, cellular networks are suffering the consequences of a mobile data traffic explosion. Operators and standard development organizations have adopted Heterogeneous Network (HetNet) solutions which offload traffic from cellular networks to other Radio Access Technologies to reduce the overloaded situation. To minimize service blocking ratios, HetNets need to be designed with an efficient approach to allocate heterogeneous channel resources. This work proposes a Combined UE and BS Information scheme (CUBI), which follows the standard architecture proposed by 3rd generation partnership project. We compare the impacts of using the User Equipment (UE) information only, and Base Stations (BS) information only to achieve network selection. We then propose a 2-round solution that benefits from both UE and BS information. The UE-information scheme is based on channel qualities of all available networks, and using it selects the network which provides the highest data rate. On the other hand, the BS-information scheme (BSI) is based on traffic loads of all UEs to allocate bandwidth, and using BSI will prevent BSs from overload situations. The simulation results demonstrate that CUBI can decrease service blocking ratios by 26 and 8.9 %, compared to schemes separately using US and BS information, respectively.     

An efficient cluster-based communication protocol for wireless sensor networks

A wireless sensor network is a network of large numbers of sensor nodes, where each sensor node is a tiny device that is equipped with a processing, sensing subsystem and a communication subsystem. The critical issue in wireless sensor networks is how to gather sensed data in an energy-efficient way, so that the network lifetime can be extended. The design of protocols for such wireless sensor networks has to be energy-aware in order to extend the lifetime of the network because it is difficult to recharge sensor node batteries. We propose a protocol to form clusters, select cluster heads, select cluster senders and determine appropriate routings in order to reduce overall energy consumption and enhance the network lifetime. Our clustering protocol is called an Efficient Cluster-Based Communication Protocol (ECOMP) for Wireless Sensor Networks. In ECOMP, each sensor node consumes a small amount of transmitting energy in order to reach the neighbour sensor node in the bidirectional ring, and the cluster heads do not need to receive any sensed data from member nodes. The simulation results show that ECOMP significantly minimises energy consumption of sensor nodes and extends the network lifetime, compared with existing clustering protocol.     

面向能量受限传感网的最优数据收集策略

本文研究了能量受限条件下无线传感器网络(wireless sensor networks,WSNs)的最优数据收集策略问题.首先,传感器节点周期性采集数据并通过卡尔曼滤波器(Kalman filter,KF)对信息进行预处理以滤除噪声.其次,考虑到通信为主要耗能环节,设计最优数据发送策略令节点在特定轮内发送数据,使得满足网络生存周期前提下,基站获得的数据精度最高.具体来说,针对单跳网络,给出可使基站误差方差最小化的数据发送策略;在此基础上,进一步提出面向多跳网络的改进数据发送策略.最后,利用仿真和原型实验验证所提策略的有效性.     

基于选播机制的无线Mesh网络网关选取路由算法

提出了一种基于选播的无线Mesh网络网关选取模型和相应的网关选取路由算法。该模型将所有网关节点抽象成一个选播组,将所有的网关节点组成一个网关树,实现对网关组成员的管理与维护。网关选取路由算法以时延为度量,通过有效的选播机制自适应地查找"最优"网关节点为客户节点服务,以提供响应最快的高质量的因特网接入服务。实验结果表明,该算法能在合理的时间内有效地解决多网关选取问题。     

Duty cycle allocation to maximize network lifetime of wireless sensor networks with delay constraints

In wireless sensor networks, the routing control overhead could be large because multiple relays are involved in the routing operation. In order to mitigate this problem, a promising solution is to use tier‐based anycast protocols. The main shortcoming of these protocols is that they can consume a much greater amount of energy as compared with other competing protocols using deterministic routing. In this paper, we analyze, in depth, a tier‐based anycast protocol and develop a new technique of improving network lifetime. Our solution is guided by our analytic framework that consists of subtiering and a new forwarding protocol called ‘scheduling controlled anycast protocol’. We formulate the problem for finding an optimal duty cycle for each tier with a delay constraint as a minimax optimization problem and find its solution, which we show is unique. From the analytical results, we find that the network lifetime can be significantly extended by allocating a different duty cycle adaptively for each tier under a delay constraint. Through simulations, we verify that our duty cycle control algorithm enhances the network lifetime by approximately 70% in comparison with an optimal homogeneous duty cycle allocation. Copyright © 2012 John Wiley & Sons, Ltd.