Outage and energy-efficiency analysis of cognitive radio networks: A stochastic approach to transmit antenna selection

Cognitive radio networks have recently attracted significant research attention owing to their promise for application in future cellular communication. In this light, given the intense power consumption of wireless networks, considerable research is now being directed at designing random cognitive radio networks with enhanced energy efficiency. In this study, we investigate the outage probability and energy efficiency in a cognitive radio network, modeling the locations of the primary users and cognitive users as a Poisson point process. We derive closed-form expressions for the outage probability and energy efficiency with consideration of the probabilities of unoccupied (not utilized by the primary users) channel selection and successful transmission for imperfect detection in an interference-limited environment of cognitive radio network. Furthermore, we propose a transmit antenna selection method for the cognitive transmitter in such networks and accordingly develop closed-form expressions for the outage probability and energy efficiency. The study reported here highlights the importance of combining the capabilities of unoccupied channel selection and successful transmission in cognitive radio networks to achieve optimal performance regarding outage probability and energy efficiency. In terms of energy efficiency, there is an optimal threshold that maximizes the energy efficiency. For implementation in transmit antenna selection, the outage probability can be significantly decreased by increasing the number of transmit antennas, even though the energy efficiency is maximized at the target outage probability.     

Energy Efficient Sleep Schedule with Service Coverage Guarantee in Wireless Sensor Networks

Service oriented architecture has been proposed to support collaborations among distributed wireless sensor network (WSN) applications in an open dynamic environment. However, WSNs are resource constraint, and have limited computation abilities, limited communication bandwidth and especially limited energy. Fortunately, sensor nodes in WSNs are usually deployed redundantly, which brings the opportunity to adopt a sleep schedule for balanced energy consumption to extend the network lifetime. Due to miniaturization and energy efficiency, one sensor node can integrate several sense units and support a variety of services. Traditional sleep schedule considers only the constraints from the sensor nodes, can be categorized to a one-layer (i.e., node layer) issue. The service oriented WSNs should resolve the energy optimization issue considering the two-layer constraints, i.e., the sensor nodes layer and service layer. Then, the one-layer energy optimization scheme in previous work is not applicable for service oriented WSNs. Hence, in this paper we propose a sleep schedule with a service coverage guarantee in WSNs. Firstly, by considering the redundancy degree on both the service level and the node level, we can get an accurate redundancy degree of one sensor node. Then, we can adopt fuzzy logic to integrate the redundancy degree, reliability and energy to get a sleep factor. Based on the sleep factor, we furthermore propose the sleep mechanism. The case study and simulation evaluations illustrate the capability of our proposed approach.     

Top-k query evaluation in sensor networks under query response time constraint

Top-k query in a wireless sensor network is to find the k sensor nodes with the highest sensing values. To evaluate the top-k query in such an energy-constrained network poses great challenges, due to the unique characteristics imposed on its sensors. Existing solutions for top-k query in the literature mainly focused on energy efficiency but little attention has been paid to the query response time and its effect on the network lifetime. In this paper we address the query response time and its effect on the network lifetime through the study of the top-k query problem in sensor networks with the response time constraint. We aim at finding an energy-efficient routing tree and evaluating top-k queries on the tree such that the network lifetime is significantly prolonged, provided that the query response time constraint is met too. To do so, we first present a cost model of energy consumption for answering top-k queries and introduce the query response time definition. We then propose a novel joint query optimization framework, which consists of finding a routing tree in the network and devising a filter-based evaluation algorithm for top-k query evaluation on the tree. We finally conduct extensive experiments by simulation to evaluate the performance of the proposed algorithms, in terms of the total energy consumption, the maximum energy consumption among nodes, the query response time, and the network lifetime. The experimental results showed that there is a non-trivial tradeoff between the query response time and the network lifetime, and the joint query optimization framework can prolong the network lifetime significantly under a specified query response time constraint.     

Lifetime maximizing dynamic energy efficient routing protocol for multi hop wireless networks

Energy efficiency is recognized as a critical problem in wireless networks. Many routing schemes have been proposed for finding energy efficient routing paths with a view to extend lifetime of the networks – however it has been observed that the energy efficient path depletes quickly. Further, an unbalanced distribution of energy among the nodes may cause early death of nodes as well as network. Hence, balancing the energy distribution is a challenging area of research in wireless networks. In this paper we propose an energy efficient scheme that considers the node cost of nodes for relaying the data packets to the sink. The node cost considers both the remaining energy of the node as well as energy efficiency. Using this parameter, an energy efficient routing algorithm is proposed which balances the data traffic among the nodes and also prolongs the network lifetime. Simulation shows that proposed routing scheme improves energy efficiency and network lifetime than widely used methods viz., Shortest Path Tree (SPT) and Minimum Spanning Tree (MST) based PEDAP, Distributed Energy Balanced Routing (DEBR) and Shortest Path Aggregation Tree Based Routing Protocol.     

Energy-efficient topology control algorithm for maximizing network lifetime in wireless sensor networks with mobile sink

Uneven energy consumption is an inherent problem in wireless sensor networks characterized by multi-hop routing and many-to-one traffic pattern. Such unbalanced energy dissipation can significantly reduce network lifetime. In this paper, we study the problem of prolonging network lifetime in large-scale wireless sensor networks where a mobile sink gathers data periodically along the predefined path and each sensor node uploads its data to the mobile sink over a multi-hop communication path. By using greedy policy and dynamic programming, we propose a heuristic topology control algorithm with time complexity O(n(m + n log n)), where n and m are the number of nodes and edges in the network, respectively, and further discuss how to refine our algorithm to satisfy practical requirements such as distributed computing and transmission timeliness. Theoretical analysis and experimental results show that our algorithm is superior to several earlier algorithms for extending network lifetime.     

Detection and replacement of a failing node in the wireless sensors networks

The lifetime in a wireless network, in particular a wireless sensor network, depends strongly on the connectivity factor between nodes. Several factors can be at the origin of a connectivity rupture such as: lack of energy on a significant node level, infection of a vital node by a malevolent code and a logical or physical failure of a primary node. This rupture can lead in some cases to a reconfiguration of the network by generating a prejudicial overhead or in other cases to a failure of the mission assigned to the network. In this paper, we propose a DRFN approach (Detection and Replacement of a Failing Node) for the connectivity maintenance by carrying out a replacement chain according to a distributed algorithm. Through simulation, we have shown our approach efficiency. Compared with similar work, our proposed approach consumes less energy, and improves the percentage of reduction in field coverage.     

Consistency-driven data quality management of networked sensor systems

With more and more real deployments of wireless sensor network applications, we envision that their success is nonetheless determined by whether the sensor networks can provide a high quality stream of data over a long period. In this paper, we propose a consistency-driven data quality management framework called Orchis that integrates the quality of data into an energy efficient sensor system design. Orchis consists of four components, data consistency models, adaptive data sampling and process protocols, consistency-driven cross-layer protocols and flexible APIs to manage the data quality, to support the goals of high data quality and energy efficiency. We first formally define a consistency model, which not only includes temporal consistency and numerical consistency, but also considers the application-specific requirements of data and data dynamics in the sensing field. Next, we propose an adaptive lazy energy efficient data collection protocol, which adapts the data sampling rate to the data dynamics in the sensing field and keeps lazy when the data consistency is maintained. Finally, we conduct a comprehensive evaluation to the proposed protocol based on both a TOSSIM-based simulation and a real prototype implementation using MICA2 motes. The results from both simulation and prototype show that our protocol reduces the number of delivered messages, improves the quality of collected data, and in turn extends the lifetime of the whole network. Our analysis also implies that a tradeoff should be carefully set between data consistency requirements and energy saving based on the specific requirements of different applications.     

Traffic load analysis and its application to enhancing longevity on IEEE 802.15.4/ZigBee Sensor Network

Power consumption is an interesting challenge to prolong the operational lifetime on IEEE 802.15/ZigBee Sensor Network (ZSN). ZigBee routers closer to the ZigBee coordinator (ZC) have a larger forwarding traffic burden and consume more energy than devices further away from the ZC. The whole operational lifetime of ZSN is deteriorated because of such an uneven node power consumption patterns, leading to what is known as an energy hole problem (EHP) around the ZC. In this article the average load traffic pattern for the ZSN has been explored and derived in terms of closed-form mathematical expressions. Also we propose a novel power-saving scheme to alleviate the EHP based on the N-policy M/G/1 queuing model. Having a counter (N) that controls the triggering radio server can reduce power consumption of a generic node. With little management cost, the proposed queue-based power-saving technique can be applied to prolong the lifetime of sensor network economically and effectively. For the proposed queue-based model, mathematical framework on performance measures has been formulated. Focusing on ZigBee routers deployed at the innermost shell of ZSN, numerical and network simulation results validate that the proposed approach indeed provides a feasibly cost-effective approach for lifetime elongation of wireless sensor networks.     

Edge betweenness centrality: A novel algorithm for QoS-based topology control over wireless sensor networks

In this paper we propose a novel topology-control algorithm, called edge betweenness centrality (EBC). EBC is based on the concept of betweenness centrality, which has been first introduced in the context of social network analysis (SNA), and measures the “importance” of each node in the network. This information allows us to achieve high quality of service (QoS) in wireless sensor networks by evaluating relationships between entities of the network (i.e., edges), and hence identifying different roles among them (e.g., brokers, outliers), thus controlling information flow, message delivery, latency and energy dissipation among nodes. The experimental evaluation and analysis of EBC in comparison to other state-of-the-art topology control algorithms shows that our algorithm outperforms the competitor ones in all observed cases.     

Efficient spectrum utilization via cross-layer optimization in distributed cognitive radio networks

In this paper, we propose a novel spectrum allocation approach for distributed cognitive radio networks. Cognitive radio systems are capable of sensing the prevailing environmental conditions and automatically adapting its operating parameters in order to enhance system and network performance. Using this technology, our proposed approach optimizes each individual wireless device and its single-hop communication links using the partial operating parameter and environmental information from adjacent devices within the wireless network. Assuming stationary wireless nodes, all wireless communication links employ non-contiguous orthogonal frequency division multiplexing (NC-OFDM) in order to enable dynamic spectrum access (DSA). The proposed approach will attempt to simultaneously minimize the bit error rate, minimize out-of-band (OOB) interference, and maximize overall throughput using a multi-objective fitness function. Without loss in generality, genetic algorithms are employed to perform the actual optimization. Several assisting processes have also been devised to make the approach more efficient and robust. Such procedure is able to reduce BER by an order of magnitude.     

Energetic sustainability of routing algorithms for energy-harvesting wireless sensor networks

A new class of wireless sensor networks that harvest power from the environment is emerging because of its intrinsic capability of providing unbounded lifetime. While a lot of research has been focused on energy-aware routing schemes tailored to battery-operated networks, the problem of optimal routing for energy harvesting wireless sensor networks (EH-WSNs) has never been explored. The objective of routing optimization in this context is not extending network lifetime, but maximizing the workload that can be autonomously sustained by the network.In this work we present a methodology for assessing the energy efficiency of routing algorithms for networks whose nodes drain power from the environment. We first introduce the energetic sustainability problem, then we define the maximum energetically sustainable workload (MESW) as the objective function to be used to drive the optimization of routing algorithms for EH-WSNs.We propose a methodology that makes use of graph algorithms and network simulations for evaluating the MESW starting from a network topology, a routing algorithm and a distribution of the environmental power available at each node. We present a tool flow implementing the proposed methodology and we show comparative results achieved on several routing algorithms.Experimental results highlight that routing strategies that do not take into account environmental power do not provide optimal results in terms of workload sustainability. Using optimal routing algorithms may lead to sizeable enhancements of the maximum sustainable workload. Moreover, optimality strongly depends on environmental power configurations. Since environmental power sources change over time, our results prompt for a new class of routing algorithms for EH-WSNs that are able to dynamically adapt to time-varying environmental conditions.     

Reinforcement learning for context awareness and intelligence in wireless networks: Review, new features and open issues

In wireless networks, context awareness and intelligence are capabilities that enable each host to observe, learn, and respond to its complex and dynamic operating environment in an efficient manner. These capabilities contrast with traditional approaches where each host adheres to a predefined set of rules, and responds accordingly. In recent years, context awareness and intelligence have gained tremendous popularity due to the substantial network-wide performance enhancement they have to offer. In this article, we advocate the use of reinforcement learning (RL) to achieve context awareness and intelligence. The RL approach has been applied in a variety of schemes such as routing, resource management and dynamic channel selection in wireless networks. Examples of wireless networks are mobile ad hoc networks, wireless sensor networks, cellular networks and cognitive radio networks. This article presents an overview of classical RL and three extensions, including events, rules and agent interaction and coordination, to wireless networks. We discuss how several wireless network schemes have been approached using RL to provide network performance enhancement, and also open issues associated with this approach. Throughout the paper, discussions are presented in a tutorial manner, and are related to existing work in order to establish a foundation for further research in this field, specifically, for the improvement of the RL approach in the context of wireless networking, for the improvement of the RL approach through the use of the extensions in existing schemes, as well as for the design and implementation of RL in new schemes.     

Distributed adaptive top-k monitoring in wireless sensor networks

Top-k monitoring queries are useful in many wireless sensor network applications. A query of this type continuously returns a list of k ordered nodes with the highest (or lowest) sensor readings. To process these queries, a well-known approach is to install a filter at each sensor node to avoid unnecessary transmissions of sensor readings. In this paper, we propose a new top-k monitoring method, named Distributed Adaptive Filter-based Monitoring. In this method, we first propose a new query reevaluation algorithm that works distributedly in the network to reduce the communication cost of sending probe messages. Then, we present an adaptive filter updating algorithm which is based on predicted benefits to lower down the transmission cost of sending updated filters to the sensor nodes. Experimental results on real data traces show that our proposed method performs much better than the other existing methods in terms of both network lifetime and average energy consumption.     

A framework for use of wireless sensor networks in forest fire detection and monitoring

Forest fires are one of the main causes of environmental degradation nowadays. Current surveillance systems for forest fires lack in supporting real-time monitoring of every point of a region at all times and early detection of fire threats. Solutions using wireless sensor networks, on the other hand, can gather sensory data values, such as temperature and humidity, from all points of a field continuously, day and night, and, provide fresh and accurate data to the fire-fighting center quickly. However, sensor networks face serious obstacles like limited energy resources and high vulnerability to harsh environmental conditions, that have to be considered carefully. In this paper, we propose a comprehensive framework for the use of wireless sensor networks for forest fire detection and monitoring. Our framework includes proposals for the wireless sensor network architecture, sensor deployment scheme, and clustering and communication protocols. The aim of the framework is to detect a fire threat as early as possible and yet consider the energy consumption of the sensor nodes and the environmental conditions that may affect the required activity level of the network. We implemented a simulator to validate and evaluate our proposed framework. Through extensive simulation experiments, we show that our framework can provide fast reaction to forest fires while also consuming energy efficiently.     

InCloud: a cloud-based middleware for vehicular infotainment systems

Smart vehicles form pervasive environment to enhance user experience through multimedia enabled infotainment systems. In order to realize effective infotainment system for vehicles, we need to have context-aware applications that use latest (live) information for enhanced user experience. Such latest information is abundantly available on the Internet due to explosive growth of Web 3.0, which can be accessed through wireless communication infrastructures such as VANETs and LTE. In this paper we propose a cloud-based middleware framework, InCloud, for vehicular infotainment application development. The proposed framework follows service oriented architecture in which data filtering and fusion functionalities are delegated to the cloud. Data filtering and fusion reduce the data flow over wireless link. Furthermore, because most of the processing is done on the cloud, the client becomes lightweight and loosely coupled with Internet resources and underlying platforms in vehicles. We also propose a class-based fusion method for combining information from multiple resources on the Internet. The efficacy of the proposed framework is validated by developing three infotainment applications for vehicles: context-aware music, news, and an enhanced Direction (eDirection) application.

     

Data aggregation framework for energy-efficient WirelessHART networks

WirelessHART has become an industrial standard for robust and real-time wireless monitoring and control. While energy-efficiency is one of the key design considerations for networks with battery-operated devices, data aggregation has been widely studied in the wireless sensor network (WSN) environments to reduce the traffic and prolong the lifetime of the network. However, existing data aggregation techniques cannot be applied directly to WirelessHART networks due to the multi-channel Time Synchronized Mesh Protocol (TSMP) and the superframe-based communication slot scheduling in WirelessHART. In this work, we propose a data aggregation framework for energy-efficient and real-time WirelessHART communication. In particular, we consider aggregation as a factor during the link selection procedure of the graph routing to improve the chance of data fusion at intermediate routing nodes and reduce the total number of message transmissions. Furthermore, a greedy-based heuristic is applied during the superframe construction phase to allocate package transmissions whose data can be aggregated at intermediate routing nodes into nearby time slots. During the superframe re-scheduling, we make sure that the predefined end-to-end deadline for each package is satisfied as long as the entire network is schedulable without data aggregation. Experimental results show that compared with existing WirelessHART routing algorithms, our proposed framework has significantly improvement on the energy saving and prolongs the overall lifetime of the network.     

A Gossip-Based Energy Conservation Protocol for Wireless Ad Hoc and Sensor Networks

In this paper, we present an energy conservation scheme for wireless ad hoc and sensor networks using gossiping to place nodes in an energy saving sleep state. The technique is termed the Gossip-based Sleep Protocol (GSP). With GSP, each node randomly goes to sleep for some time with gossip sleep probability p. GSP is based on the observation that in a well connected network there are usually many paths between a source and destination, so a percentage of nodes can be in an energy conserving sleep mode without losing network connectivity. GSP needs few operations, scales to large networks and does not require a wireless node to maintain the states of other nodes. We propose two versions of GSP, one for synchronous networks and one for asynchronous networks, and afterward extend GSP to adapt to network traffic conditions. We show the advantages of the GSP approach through both simulations and analysis.     

A Methodology for Reliability of WSN Based on Software Defined Network in Adaptive Industrial Environment

As communication technology and smart manufacturing have developed, the industrial internet of things (IIoT) has gained considerable attention from academia and industry. Wireless sensor networks (WSNs) have many advantages with broad applications in many areas including environmental monitoring, which makes it a very important part of IIoT. However, energy depletion and hardware malfunctions can lead to node failures in WSNs. The industrial environment can also impact the wireless channel transmission, leading to network reliability problems, even with tightly coupled control and data planes in traditional networks, which obviously also enhances network management cost and complexity. In this paper, we introduce a new software defined network (SDN), and modify this network to propose a framework called the improved software defined wireless sensor network (improved SD-WSN). This proposed framework can address the following issues. 1) For a large scale heterogeneous network, it solves the problem of network management and smooth merging of a WSN into IIoT. 2) The network coverage problem is solved which improves the network reliability. 3) The framework addresses node failure due to various problems, particularly related to energy consumption. Therefore, it is necessary to improve the reliability of wireless sensor networks, by developing certain schemes to reduce energy consumption and the delay time of network nodes under IIoT conditions. Experiments have shown that the improved approach significantly reduces the energy consumption of nodes and the delay time, thus improving the reliability of WSN.      

Push-and-track: Saving infrastructure bandwidth through opportunistic forwarding

Major wireless operators are nowadays facing network capacity issues in striving to meet the growing demands of mobile users. At the same time, 3G-enabled devices increasingly benefit from ad hoc radio connectivity (e.g., WiFi). In this context of hybrid connectivity, we propose Push-and-track, a content dissemination framework that harnesses ad hoc communication opportunities to minimize the load on the wireless infrastructure while guaranteeing tight delivery delays. It achieves this through a control loop that collects user-sent acknowledgements to determine if new copies need to be re-injected into the network through the 3G interface. Push-and-Track is flexible and can be applied to a variety of scenarios, including periodic message flooding and floating data. For the former, this paper examines multiple strategies to determine how many copies of the content should be injected, when, and to whom; for the latter, it examines the achievable offload ratio depending on the freshness constraints. The short delay-tolerance of common content, such as news or road traffic updates, make them suitable for such a system. Use cases with a long delay-tolerance, such as software updates, are an even better fit. Based on a realistic large-scale vehicular dataset from the city of Bologna composed of more than 10,000 vehicles, we demonstrate that Push-and-Track consistently meets its delivery objectives while reducing the use of the 3G network by about 90%.