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In wireless network virtualization processes, load imbalance in substrate nodes and substrate links may significantly reduce the number of virtual network requests served by a substrate network. In addition, load imbalance also impairs resource utilization and increases embedding cost of the virtual networks. To tackle these issues, this work proposes a re-embedding scheme for virtual node (RSVN). Based on the load condition of a substrate network, RSVN first determines whether to start re-embedding procedure or not, and then selects proper re-embedded virtual nodes according to their re-embedding factors. Next, RSVN re-embeds the selected virtual nodes to balance the loads of a wireless substrate network. The extensive simulation results showed that RSVN improves the acceptance ratio of virtual network requests, increases the resource utilization of substrate networks, and reduces the average embedding cost of the entire virtual networks. [ABSTRACT FROM AUTHOR]

In wireless powered communication networks (WPCNs), base station (BS) and power beacons (PBs) can offer supplement power for uplink transmission of user equipments (UEs). However, the aggregate power consumption of massively deployed PBs may exceed that of a BS. We propose a non-uniform deployment scheme for PBs in WPCNs, where a cell is divided into inner and outer areas, such that BS and PBs can cooperate to power UEs. To be more specific, a BS located in the center of a cell provides downlink power supply for the inner area UEs and uplink information decoding for all the UEs in the cell; while the PBs power UEs in the outer area. With multiple antennas, maximum ratio transmission and maximum ratio combining are adopted for downlink energy beamforming and uplink information reception. Considering a finite area of the network, we derive the distribution of the distance from a non-center-located UE to its nearest PB in the outer area. An optimization problem is formulated to minimize total average power consumption while satisfying BS average transmission power constraint and coverage probability threshold. Moreover, coverage probability is derived for performance evaluation. The numerical results show that the power consumption of the proposed scheme is reduced significantly compared to PB-only WPCNs. [ABSTRACT FROM AUTHOR]

Radio frequency energy transport is a viable technology to power small-sized sensors in wireless industrial sensor networks (WISNs). 60 GHz wireless communication emerges as one of the leading technologies for next generation wireless networks, and it can also be useful to build up WISNs. In this article, we investigate the efficiency of RF energy transport in WISNs with 60 GHz communications. We present a spherical antenna design with supported analytics on directional antennas in 60 GHz wireless communications. We introduce and solve the spherical distribution problem of directional antennas to maximize the energy transport efficiency in WISNs. The extensive simulations given in this article verify that the proposed antenna configuration performs much better than the existing solutions in WISN applications. [ABSTRACT FROM PUBLISHER]

Physical-layer network coding (PNC) is an attractive technique because it can significantly enhance the throughput of a wireless network with a relatively simple implementation, as compared with conventional network coding (NC) schemes. In this paper, we first investigate PNC with frequency shift keying (FSK) through the derivation of its bit-error rate (BER) and capacity, as well as the impact of imperfect synchronization to the overall performance. Based on the discussions on PNC with 2FSK, we then further expand the analysis to PNC with $M$-ary FSK (MFSK). Theoretical results indicate that the BER performance of PNC with 2FSK is a bit worse than that of PNC with 2PSK. As the constellation dimension $M$ increases, the BER performance of PNC with $M$-ary FSK improves at the expense of bandwidth efficiency degradation. [ABSTRACT FROM PUBLISHER]

Cognitive radio (CR) is a critical technology for improving spectrum utilization and solving the radio spectrum scarcity problem. In CR devices, spectrum sensing is important to implement opportunistic spectrum access. Many spectrum sensing schemes have been proposed, including uncooperative, cooperative, centralized, and distributed algorithms. However, they aimed to obtain a global consensus sensing result, which may not always be possible in large-scale cognitive radio networks (CRNs) due to heterogeneous spectrum availability in different areas. Hence, some new spectrum sensing schemes should be designed to discover idle heterogeneous spectrum in CRNs. In this paper, we propose an intelligent cooperative spectrum sensing algorithm based on a non-parametric Bayesian learning model, namely the hierarchical Dirichlet process, which groups spectrum sensing data without the need to know the number of hidden spectrum states, and discovers a common sparse spectrum within each group. Furthermore, a concisely distributed information exchange scheme is designed, where intra-cluster and inter-cluster spectrum information is shared for global spectrum cognition. Experimental results show that the proposed algorithm can exploit the spatial relationship among sensed data to achieve a better spectrum sensing performance in terms of detection probability and false alarm probability. [ABSTRACT FROM AUTHOR]

This article reviews an emerging wireless information and power transfer (WIPT) technique with an emphasis on its performance enhancement employing multi-antenna techniques. Compared to traditional wireless information transmission, WIPT faces numerous challenges. First, it is more susceptible to channel fading and path loss, resulting in a much shorter power transfer distance. Second, it gives rise to the issue of how to balance spectral efficiency for information transmission and energy efficiency for power transfer in order to obtain an optimal tradeoff. Third, there exists a security issue for information transmission in order to improve power transfer efficiency. In this context, multi-antenna techniques, e.g. energy beamforming, are introduced to solve these problems by exploiting spatial degree of freedom. This article provides a tutorial on various aspects of multi-antenna based WIPT techniques, with a focus on tackling the challenges by parameter optimization and protocol design. In particular, we investigate the WIPT tradeoffs based on two typical multi-antenna techniques: the limited feedback multi-antenna technique for short-distance transfer; and the large-scale multiple-input multiple-output (LS-MIMO, also known as massive MIMO) technique for long-distance transfer. Finally, simulation results validate the effectiveness of the proposed schemes. [ABSTRACT FROM PUBLISHER]

SUMMARY Resource reservation or the other prioritization strategies adopted by Call Admission Control (CAC) schemes in wireless networks lead to unfair resource allocation to users belonging to different service classes (SCs) due to high divergence among the respective call blocking probabilities (CBPs). In this paper, we propose dynamic optimization of probabilistic CAC (P-CAC) schemes to assure CAC fairness among users of different SCs in wireless networks. The approach is based on users utility combined with fairness optimization, aiming at dynamically determining the probability value in the P-CAC scheme. This optimal probability is adjusted to network ongoing traffic, CBPs of each SC, prioritization levels characterizing the SCs supported, and the users risk aversion, which reflects their behavior toward the perceived QoS. The existence and uniqueness of the optimal probability that leads to absolute fairness among the users of a wireless network are proven. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The performance analysis of a distributed access wireless network, in which nodes are randomly distributed [namely, a random wireless network (RWN)] over a large area, is challenging. An effective mathematical modeling of these networks can help network designers to predict whether a given set of quality-of-service (QoS) requirements can be satisfied prior to the real network deployment. Previous models for the performance analysis of multihop RWNs fail to give accurate results due to the assumption of a single-hop, limited, and a priori known number of stations; negligence of the hidden terminals; and the use of a Boolean logic collision model. In this paper, we develop an alternative probabilistic modeling approach to analyze the performance of these networks in medium-access-control (MAC) and network layers. Using the proposed modeling approach and obtained metrics, we can identify a set of optimal parameters to maximize the performance of the networks. [ABSTRACT FROM PUBLISHER]

Operational safety and security are the key concerns of the North American railroad industry. Of growing importance is the ability for real-time identification, monitoring, and fault alerting of freight railcars and their myriad components. This vision of an Internet of Things applied to the transportation industry has widespread benefits and applications. Wireless sensor networks (WSNs) have been extensively used for addressing such challenges. However, the nature of a train's arrangement requires the sensors to be placed in a long linear network topology. Given the limitations in radio transmission range and network depth of common WSN solutions like ZigBee, it is infeasible to use them for such applications. Furthermore, due to its low throughput, severe packet loss and high latency may occur, which is unacceptable, especially for failure alert messages. Therefore, we have proposed a heterogeneous multihop network approach called hybrid technology networking (HTN) that employs WiFi together with ZigBee. In this paper, we derive a mathematical model to illustrate HTNs superior performance compared with conventional ZigBee-based WSNs. Specifically, we analyze the delay and reliability of HTN and ZigBee and verify the analytical results against our simulations. [ABSTRACT FROM PUBLISHER]

Online anomaly detection (AD) is an important technique for monitoring wireless sensor networks (WSNs), which protects WSNs from cyberattacks and random faults. As a scalable and parameter-free unsupervised AD technique, $(k)$-nearest neighbor (kNN) algorithm has attracted a lot of attention for its applications in computer networks and WSNs. However, the nature of lazy-learning makes the kNN-based AD schemes difficult to be used in an online manner, especially when communication cost is constrained. In this paper, a new kNN-based AD scheme based on hypergrid intuition is proposed for WSN applications to overcome the lazy-learning problem. Through redefining anomaly from a hypersphere detection region (DR) to a hypercube DR, the computational complexity is reduced significantly. At the same time, an attached coefficient is used to convert a hypergrid structure into a positive coordinate space in order to retain the redundancy for online update and tailor for bit operation. In addition, distributed computing is taken into account, and position of the hypercube is encoded by a few bits only using the bit operation. As a result, the new scheme is able to work successfully in any environment without human interventions. Finally, the experiments with a real WSN data set demonstrate that the proposed scheme is effective and robust. [ABSTRACT FROM PUBLISHER]

Wireless sensor networks (WSNs) consist of tiny sensor nodes that communicate with each other over wireless channels, often in a hostile environment where nodes can be captured and compromised. Consequently, an adversary may launch a clone attack by replicating the captured nodes to enlarge the compromised areas employing clones. Thus, it is critical to detect clone nodes promptly for minimizing their damage to WSNs. Recently, various clone detection schemes were proposed for WSNs, considering different types of network configurations, such as device types and deployment strategies. In order to choose an effective clone detection scheme for a given sensor network, the selection criteria play an important role. In this paper, we first investigate the selection criteria of clone detection schemes with regard to device types, detection methodologies, deployment strategies, and detection ranges. We then classify the existing schemes according to the proposed criteria. Simulation experiments are conducted to compare their performances. It is concluded that it is beneficial to utilize the grid deployment knowledge for static sensor networks; the scheme using the grid deployment knowledge can save energy by up to 94.44% in comparable performance (specifically in terms of clone detection ratio and the completion time), as compared to others. On the other hand, for mobile sensor networks, no existing approach works efficiently in reducing detection error rate. [ABSTRACT FROM PUBLISHER]

The necessity to promote smart grid (SG) has been recognized with a strong consensus. The SG integrates electrical grids and communication infrastructures and forms an intelligent electricity network working with all connected components to deliver sustainable electricity supplies. Many advanced communication technologies have been identified for SG applications with a potential to significantly enhance the overall efficiency of power grids. In this paper, the challenges and applications of communication technologies in SG are discussed. In particular, we identify three major challenges to implement SG communication systems, including standards interoperability, cognitive access to unlicensed radio spectra, and cyber security. The issues to implement SG communications on an evolutional path and its future trends are also addressed. The aim of this paper is to offer a comprehensive review of state-of-the-art researches on SG communications. [ABSTRACT FROM PUBLISHER]

One of the most challenging issues in wireless sensor networks is to meet the requirements of coverage and connectivity under given energy constraints. Most existing coverage and connectivity algorithms work to form tree networks when sensor nodes do not have location information of themselves. However, a tree topology network does not perform well in terms of energy efficiency and scalability if compared with a cluster network. In this paper, a novel topology control algorithm called Adaptive Random Clustering (ARC) is proposed to form a cluster network with required coverage and connectivity without location information. The performance of its coverage intensity and connectivity is analyzed based on the characteristics of cluster topology, and their proper parameters are determined. ARC inherits an excellent energy efficiency from cluster topology and avoids the collisions and overhearing of data packets. A good scalability can be achieved as only a limited number of channels are needed in ARC for a large-scale network. Furthermore, ARC can adjust the number of active nodes adaptively according to the required coverage to balance the energy consumption. Simulation results demonstrate that required coverage and connectivity can be satisfied and network lifetime is prolonged significantly. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the authors discuss various reports within the issue on topics including spectrum allocation in cellular cognitive radio networks, multiple-input multiple-output (MIMO) wireless sensor networks and multi-hop networks.

With the proliferation of mobile devices and the popularity of mobile multimedia applications, systematic research of the overall aspects of security in mobile multimedia communications is crucial. The emerging mobile wireless multimedia communications can be used for various pervasive and ubiquitous applications in consumers, businesses, and government agencies. Besides the consumer cellular systems and residential homes, mobile devices are being rapidly integrated into enterprises with the increase in bring-your-own-device (BYOD) in many organizations. As mobile multimedia communications become more popular in both the consumer and business markets, the need for tighter security is inevitable. [ABSTRACT FROM PUBLISHER]

Network coverage is one of the most critical issues to implement Wireless Sensor Networks (WSNs). It is important to find out a sensor set with maximal residual energy to cover all points of interest (PoIs). This issue was named as minimum weight sensor coverage problem (MWSCP) based on a boolean disc model and a probabilistic sensing model in the literature. In this paper, we introduce intelligent algorithms to solve this problem, yielding a better solution to MWSCP to extend network lifetime. Simulation results are conducted to demonstrate the effectiveness of our proposed algorithm in terms of network lifetime over existing algorithms. [ABSTRACT FROM PUBLISHER]