When the legacy power infrastructure is augmented by a communication infrastructure, it becomes a smart grid. This additional communication infrastructure facilitates the exchange of state and control information among different components of the power infrastructure. An Enhanced Public Key Infrastructure to Secure Smart Grid Wireless Communication Networks As a result, the power grid can operate more reliably and efficiently .Although deploying the smart grid enjoys enormous social, environmental and technical benefits, the incorporation of information and communication technologies into the power infrastructure will introduce many security challenges. For example, it is estimated that the data to be collected by the An Enhanced Public Key Infrastructure to Secure Smart Grid Wireless Communication Networks smart grid will be an order of magnitude more than that of existing electrical power systems. This increase in data collection can possibly introduce security and privacy risks. Moreover, the smart grid will be collecting new types of information that were not recorded in the past, and this can lead to more privacy issues .As shown in Fig. 1, an essential part of the smart grid will An Enhanced Public Key Infrastructure to Secure Smart Grid Wireless Communication Networks be its communication networks. This is a three-tier network which connects the different components of the smart grid together, and allows two-way information flow. The first tier connects the transmission system located at the power plant and the control centers of Neighborhood Area Network (NAN). Each NAN comprises a number of Building Area Networks (BANs) and provides them interfaces to the utility’s wide-area network. Here, BANs are customer networks and belong to the second tier of the shown system. Each BAN consists of a number of third-tier networks, Home Area Network (HANs). The HAN is a customer premises network which manages the on-demand power requirements of end users. Note that there is no standard definition of these networks yet. Their structures described above feature a practical configuration that can be found in established smart grids. An Enhanced Public Key Infrastructure to Secure Smart Grid Wireless Communication Networks While different components of the power infrastructure of the smart grid are networked together to exchange information, as illustrated in Fig. 1, there is a potential increase of the security risk of the system. For example, it will increase the complexity of the electrical power grid, which in turn can increase new security vulnerabilities. Also, the number of entry points that can be used to gain access to the electrical power system will increase when all of the components are networked together. In the remainder of this article, we mainly focus on the security of wireless communication subnetworks of the smart grid. Security in wired links can be achieved by existing techniques such as firewalls, virtual private networks, Secure Shell or other higher layer security mechanisms.

The proliferation of consumer-oriented communication technologies across the Internet and mobile communication has fostered growing attention to social networks platforms (SNs), and expedited a large-scale of SN services on the Internet. These SN services help connecting people from different geographic location, BASA: Building Mobile Ad-hoc Social Networks on Top of Android facilitating smooth function of their work, different moods of communication and socialization. Despite the widespread success of social networks, they have certain limitation which can be further boosted with the proposed new design solution. Firstly, people often demand a kind of ad-hoc social networks to strengthen local communication with proximal contact and closeness on local address. BASA: Building Mobile Ad-hoc Social Networks on Top of Android In scenarios such as conferences and expositions, the participants might exchange information and share documents with new partners. However, there is no direct way in current social networks to facilitate local social communication. Thus, the participants might give up exploiting interpersonal affinities for personal benefits. Although, face-to-face communication is a way, but it is less useful for BASA: Building Mobile Ad-hoc Social Networks on Top of Android file sharing and group discussions, where social media is the main goal on social network. Secondly, the existing SN services implicitly assume that the Internet or cellular network infrastructures are always available. This assumption, however, may not be held true at all time owing to the blind network spots, device heterogeneity, and security considerations. BASA: Building Mobile Ad-hoc Social Networks on Top of Android Thirdly, it is time-consuming to build and manage local SNs in Android platforms without general development schemes. Each service provider accomplishes local SN functions by individual schemes, incurring much repetition of work and heavy human resource costs~\cite{Katsaros}. Additionally, Google Android has provided developers with common API libraries and development tools necessary to build, test and debug applications. However, it does not provide support for local social community orchestration.Recently, the MASNs become prevalent for the ubiquitous usage in laptops, smart phones and touch PADs. The MASNsrefer to a kind of self-configuring and self-organizing social networking paradigm, which set up local social communication via mobile devices without utilizing the underlying infrastructures BASA: Building Mobile Ad-hoc Social Networks on Top of Android. They bring both convenience and challenges to SNs. On one side of the spectrum, the MASNs relax the requirement that communication infrastructures are indispensable. By short-range communication techniques such as Bluetooth and ZigBee, the MASNs establish local community. On the other side of the spectrum, they impose new challenges on SN services due to local socialization and user mobility. The related studies of MASNs cover a series of areas, mainly comprising of community detection, evolution and data transmission. There are some schemes close to our work.

THE current wireless communication networks adopt fixed spectrum access strategy. The Federal Communications Commission have found that this fixed spectrum access strategy utilizes the available frequency bands inefficiently .A promising approach of addressing this problem is Max-Min SNR Signal Energy Based Spectrum Sensing Algorithms for Cognitive Radio Networks with Noise Variance Uncertainty to deploy a cognitive radio (CR) network. One of the key characteristics of a CR network is its ability to discern the nature of the surrounding radio environment. This is performed by the spectrum sensing (signal detection) part of a CR network. The most common spectrum sensing algorithms for CR networks are matched filter, energy and cyclostationary based algorithms. Max-Min SNR Signal Energy Based Spectrum Sensing Algorithms for Cognitive Radio Networks with Noise Variance Uncertainty If the characteristics of the primary user such as modulation scheme, pulse shaping filter and packet format are known perfectly, matched filter is the optimal signal detection algorithm as it maximizes the received Signal-to-Noise Ratio (SNR). This algorithm has two major drawbacks: The first drawback is it needs dedicated receiver to detect each signal characteristics of a primary user. The second drawback is it requires perfect synchronization between the transmitter and receiver which is impossible to achieve. Max-Min SNR Signal Energy Based Spectrum Sensing Algorithms for Cognitive Radio Networks with Noise Variance Uncertainty This is due to the fact that, in general, the primary and secondary networks are administered by different operators. Energy detector does not need any information about the primary user and it is simple to implement. However, energy detector is very sensitive to noise variance uncertainty, and there is an SNR wall below which this detector can not guarantee a certain detection performan. Cyclostationary based detection algorithm is robust against noise variance uncertainty and it can reject the effect of adjacent channel interference. However, the computational complexity of this detection algorithm is high, and large number of samples are required to exploit the cyclostationarity behavior of the received signal . On the other hand, this Max-Min SNR Signal Energy Based Spectrum Sensing Algorithms for Cognitive Radio Networks with Noise Variance Uncertainty algorithm is not robust against cyclic frequency offset which can occur due to clock and timing mismatch between the transmitter and receiver . In , Eigenvalue decomposition (EVD)-based spectrum sensing algorithm has been proposed. This algorithm is robust against noise variance uncertainty but its computational complexity is high. Furthermore, for single antenna receiver, this algorithm is sensitive to adjacent channel interference signal, and for multi-antenna receiver, this algorithm requires a channel covariance matrix different from a scaled identity.

COMPLEX-VALUED (CV) artificial neural networks have attracted considerable attention from both theoretical research and practical application communities . In particular, the communication signal processing community has long been interested in neural network representations for the CV nonlinear systems as well as in inverting the CV nonlinear systems. Complex-Valued B-Spline Neural Networks for Modeling and Inverting Hammerstein Systems It is well-known that most artificial neural networks cannot be automatically extended from the real-valued (RV) domain to the CV domain because the resulting model would in general violate Cauchy–Riemann conditions, and this means that the training algorithms become unusable. A number of analytic functions were introduced for the fully CV multilayer perceptrons Complex-Valued B-Spline Neural Networks for Modeling and Inverting Hammerstein Systems. A fully CV radial basis function network was introduced in for regression and classification applications. Alternatively, the problem can be avoided using two RV artificial neural networks, one processing the real part and the other processing the imaginary part of the CV signal/system. A more challenging problem is the inversion of a CV nonlinear system, which is typically found in communication signal processing applications. Complex-Valued B-Spline Neural Networks for Modeling and Inverting Hammerstein Systems This is a much under-researched area, and a few existing methods, such as the algorithm proposed in, are not very effective in tackling practical CV signal processing problems. The RV signal processing field offers motivations and inspirations for the development of efficient techniques for modeling and inversion of the CV nonlinear systems. A popular approach to nonlinear systems modeling in the RV domain is to use block-oriented nonlinear models, which comprise the linear dynamic models and static or memoryless nonlinear functions . Complex-Valued B-Spline Neural Networks for Modeling and Inverting Hammerstein Systems In particular, the two types of RV block-oriented nonlinear models that have found wide range of applications are the Wiener model, which comprises a linear dynamical model followed by a nonlinear static transformation, and the Hammerstein model , which consists of a nonlinear static transformation followed by a linear dynamical model. Complex-Valued B-Spline Neural Networks for Modeling and Inverting Hammerstein Systems An efficient B-spline neural network approach for modeling CV Wiener systems was derived in . With its best conditioning property, the RV B-spline curve has been used in computer graphics and computer-aided geometric design .

DUE TO its potential and promising commercial and military applications, wireless localization technique has drawn extensive attention in recent years. Most of traditional wireless localization techniques, such as sensor networks localization , Lightweight Robust Device-Free Localization in Wireless Networks RFID localization , robot and pedestrian localization [4], equip the target with a wireless device which emits signals that can be detected by some anchor nodes whose locations are known a prior, and localization is realized in a cooperative way by utilizing the wireless measurements between the target and anchor nodes. However, in some applications, such as battlefield surveillance, security safeguard, and emergency rescue, the target is uncooperative, and thus it is impractical to equip the target with a wireless device. Lightweight Robust Device-Free Localization in Wireless Networks How to achieve device-free localization (DFL) without the need of equipping the target with a wireless device becomes a hallenging problem in such a scenario. Within the deployment area of the wireless networks (WNs), communications between pairs of nodes construct lots of wireless links which travel through the space. When a target moves into the area, it may shadow some of the wireless links and absorb, diffract, reflect or scatter some of the transmitted power. The shadowed links will be different when the target locates at different locations Lightweight Robust Device-Free Localization in Wireless Networks, which makes it possible to realize DFL based on the link measurements. TheDFL techniquewas originally proposed independently by Youssef et al. and Zhang et al.. Youssef et al. modeled the problem as a machine learning problem and realized DFL with a fingerprint matching method. Zhang et al. presented a signal dynamic model, and adopted the geometric method as well as the dynamic cluster based probabilistic cover algorithm to solve the DFL problem. These works make valuable exploration on the DFL problem, and prove the feasibility of making use of the shadowing effect of the Lightweight Robust Device-Free Localization in Wireless Networks wireless links to realize DFL. However, the machine learning method requires an off-line training process which is laborious and time-consuming, while the geometric method is sensitive to noises since it uses only the current observation to realize location estimation. More recently, Savazzi et al. evaluated DFL technique with plenty of experiments. Wilson et al. and Zhao et al Lightweight Robust Device-Free Localization in Wireless Networks.

SOFT frequency re-use (SFR) pattern maximizes spectrum utilization in Long Term Evolution (LTE) networks by allowing all macrocell basestations (MBSs) to perform transmission over the entire available spectrum However, considering that LTE also employs micro-, Confederation Based RRM with Proportional Fairness for Soft Frequency Reuse LTE Networks pico- and femtocells basestations (BSs), as small cell BSs (SBSs) within each macrocell, when all subcarriers are occupied, SFR leads to more interference at the SBS’s user equipments (UEs). Furthermore, the presence of femtocells, as low cost alternative to picocells, results in additional interference as they are installed and controlled by the end-user . Therefore, in order to implement the SFR approach effectively Confederation Based RRM with Proportional Fairness for Soft Frequency Reuse LTE Networks in LTE heterogeneous cellular networks (HetNets), all BSs must have adaptive interference avoidance capability . In 4G HetNets, which employ orthogonal frequency division multiple access (OFDMA), downlink interference is practically reduced using radio resource management (RRM). This includes frequency spectrum allocation and power control ,, where in the case of interfering BSs, spectrum allocation minimises interference by allocating different subsets of subcarriers to those BSs Confederation Based RRM with Proportional Fairness for Soft Frequency Reuse LTE Networks This however reduces the ability of the interfering BSs to fully exploit multiuser diversity and consequently reduces the achievable throughput. Thus, in order to capture this, it is important to evaluate the combined performance of RRM and scheduling together. The most popular scheduling algorithms in OFDMA systems include maximum sum rate (MSR), maximum fairness (MF), proportional rate constraints (PRC), proportional fairness (PF) and the cumulative distribution function based scheduling policy , Confederation Based RRM with Proportional Fairness for Soft Frequency Reuse LTE Networks , where it retains a similar characteristic with PF scheduler that maximises multiuser diversity and maximises users fairness. Due to this reason, PF based scheduler is commonly applied in the cellular environment . Although fairness Confederation Based RRM with Proportional Fairness for Soft Frequency Reuse LTE Networks of a system can be assessed with proportion of resources assigned to a user with some normalisation factor [10], this paper interest in assessing the fairness in terms of quality of service improvement. In general, OFDMA RRMs can be classified into three categories, which are, distributed, centralized and self-organizing network. Distributed RRM works by allowing each SBS to allocate its UEs’ subcarriers based on measurements of the interference received , while the centralized RRM uses a central node to compute the subcarriers allocation for all UEs. On the other hand, SON RRM utilizes a number of functions to manage the resource. Often, SON RRM uses both the distributed and centralized approach to reduce interference.

CELLULAR network operators face hurdles in supporting the escalating growth in wireless data traffic due to spectrum scarcity. Joint Optimization of Clustering and Cooperative Beamforming in Green Cognitive Wireless Networks To tackle this challenge, cognitive radio has been proposed to improve the spectrum efficiency by allowing a secondary system to opportunistically use a spectrum band licensed to a primary system provided that the former respect the interference limits imposed by the later. Joint Optimization of Clustering and Cooperative Beamforming in Green Cognitive Wireless Networks However, spectrum sharing and interference mitigation are great challenges when the BSs perform their cognitive function individually. In this work, we thus consider the cooperation between cognitive BSs. In particular, we focus on the cooperative beamforming technique, also known as coordinated multipoint transmission (CoMP), which was first proposed to improve the performance of cell-edge users . Joint Optimization of Clustering and Cooperative Beamforming in Green Cognitive Wireless Networks When cognitive BSs cooperate, not only can they reap larger capacity and diversity gains, but also they mitigate the interference to primary users more effectively. However, these benefits of CoMP come with significant costs . First, the cooperating BSs must be connected by a backhaul through which they exchange the channel knowledge and user data. Second, cooperation largely increase the energy consumption due to the extra signal processing . However, energy efficiency has become a central issue for operators as they seek to decrease their carbon footprint and operating costs . Although the theoretical benefits and practical issues of CoMP have been studied in many works, only few have looked into its energy efficiency. Joint Optimization of Clustering and Cooperative Beamforming in Green Cognitive Wireless Networks An energy consumption model for BS cooperation has just been recently developed in. With that model, the authors in analyzed the energy efficiency of an idealized CoMP system and concluded that the cooperative processing power must be kept low for CoMP to provide an energy efficiency gain. This raises questions on when and how BSs should form clusters and cooperate. Joint Optimization of Clustering and Cooperative Beamforming in Green Cognitive Wireless Networks When the service requirements is high, cooperation may help the BSs to better serve their users and protect primary users from interference. Otherwise, they should use a simpler coordination strategy to save energy.

LONG-TERM evolution-advanced (LTE-A) techniques are proposed by the 3rd generation partnership project (3GPP) to provide higher spectrum efficiency and data rate. According to the technical report from 3GPP the downlink and uplink peak data rates are respectively required to achieve Femtocell Access Strategies in Heterogeneous Networks using a Game Theoretical Framework Gbps anMbps in order to fulfill the quality-of-service (QoS) requirement for the user equipment (UE). For achieving these objectives, imposing additional low-power base stations (BSs) into the original networks naturally becomes a feasible solution for increasing the spectrum efficiency and data rate. On the other hand, according to the statistical data in , it Femtocell Access Strategies in Heterogeneous Networks using a Game Theoretical Framework is expected that there will be nearly 90% of data services and 60% of phone calls taken place in indoor environments. Hence, femtocell BSs (fBSs) with the properties of short-range, lowpower, low-cost, and plug-and-play are designed to connect into the end user’s broadband line in order to provide high throughput and QoS for the UEs. Moreover, installation of fBSs can share the traffic load of its coexisting macrocell BSs. For the macrocell/femtocell heterogeneous networks Het- Nets, it has been studied in that co-channel deployment Femtocell Access Strategies in Heterogeneous Networks using a Game Theoretical Framework of frequency spectrum can achieve higher system throughput than independent channel deployment because of spectrum reuse. However, critical challenge associated with femtocell technology is the co-channel interference if the fBSs utilize the same frequency spectrum as the overlay mBSs, especially in the case that fBSs are operated in the closed access mode. Note that the closed and open access modes are two different access methods for the femtocell. The closed access mode only allows specific UEs that possess proper authorization, i.e., subscribers, to access the corresponding fBS. In general, subscribers are the UEs who purchase closed access fBS in order Femtocell Access Strategies in Heterogeneous Networks using a Game Theoretical Framework to improve their own throughput; while the nonsubscribers are prohibited to access the closed accessed fBS. On the other hand, the open access mode provides all the UEs with the permission to connect and access the fBS. One severe problem for this type of HetNets is that the fBS will produce strong interference to those UEs that are situated close by this Fbs but not connect to it. Apparently, this problem tends to occur in closed access mode since those nonsubscribers close to the fBS are not allowed to access it. Note that for the closed access mode, nonsubscribers are defined as the UEs who are not permitted to access the fBS; while subscribers represent those UEs that are authorized and allowed to connect with the fBS.

WIRELESS sensor networks (WSNs) have attracted tremendous attention for their mission-driven development and deployment. Improving Spectrum Efficiency via In-Network Computations in Cognitive Radio Sensor Networks For a large-scale WSN comprising lots of sensors, providing an efficient spectrum sharing with existing wireless networks is surely a trend. As facing the increasing spectrum demand of wireless services and devices , cognitive radio technology is widely employed to enhance spectrum utilization . Specifically, exploiting WSNs for smart grid applications , spectrum-aware technique is recognized as a promising solution to enable reliable Improving Spectrum Efficiency via In-Network Computations in Cognitive Radio Sensor Networks and low-cost remote monitoring for smart grids. To fully exploit this technology especially for large WSNs , more concurrent transmission opportunities within given spectrum are desired to realize spatial reuse of spectrum. In addition, maintaining reliable data transportation on top of numerous opportunistic links in cognitive (radio) multi-hop sensor networks becomes an essential requirement to bring the spectrum efficiency into reality. However, Improving Spectrum Efficiency via In-Network Computations in Cognitive Radio Sensor Networks as indicated by , there exists an significant end-to-end delay for greater network diameter in large cognitive machine networks and prevents practical applications. Thus, it becomes a great challenge to support an effective end-to-end quality-of-service (QoS) guarantee with regards of reliable communications in cognitive radio sensor networks (CSNs), while such likely technology is applicable for machine-to-machine communications, cyberphysical systems , and spectrum-sharing WSNs. Improving Spectrum Efficiency via In-Network Computations in Cognitive Radio Sensor Networks To achieve efficient spectrum management for cognitive radios, it is often done via forming the allocation optimization problems, such as spectrum or resource block allocation, user-based station assignment, and so on. Regarding multi-channel cognitive radio networks, time-spectrum blocks are allocated by constructing the subset of the good assignments and therefore obtain the suboptimal from given assignments Improving Spectrum Efficiency via In-Network Computations in Cognitive Radio Sensor Networks. A CSMA-based multi-channel MAC protocol is proposed by that optimizes the throughput performance for co-existing multiple systems. A distributed multi-channel MAC protocol is further proposed by for energy-efficient communication in multi-hop cognitive radio networks. Above efforts only focus on the efficient allocation of primary systems’ (PSs’) spectrum holes.

Reliance of society on being connected anytime and anywhere via many kinds of devices utilizing an enormously complicated telecommunications infrastructure exposes its vulnerability during disasters. Resiliency of the communication infrastructure during and after earthquakes, Vehicles as Information Hubs During Disasters: Glueing Wi-Fi to TV White Space to Cellular Networks hurricanes, floods and other natural or man-made disasters has become one of the foremost issues both for governments and private telecommunications carriers. Flow of emergency aid to areas affected by a disaster hinges on timely information coming from those areas. The infrastructure including cellular operations, might get disrupted either locally or in very wide areas due to a myriad of reasons ranging from base station power outages to equipment failures, Vehicles as Information Hubs During Disasters: Glueing Wi-Fi to TV White Space to Cellular Networks from collapsed antennas to operator level call prioritization policies. Moreover, Wi-Fi hotspot and access point connectivity might be lost due to similar causes. This, in turn, instantly renders expensive and multi-functional gadgets such as smart phones, tablets, personal computers and countless other communication devices useless. This hypothetical sounding scenario is exactly what happened during and after the Great East Japan Earthquake in March 2011 leaving scores of people hopelessly trying Vehicles as Information Hubs During Disasters: Glueing Wi-Fi to TV White Space to Cellular Networks to reach their families, relatives and friends over a nonfunctioning or partially functioning network. The following is a brief description of the system and the flow of events during the demonstration which was presented at the 20th ITS World Congress Tokyo 2013. We showed that during disasters vehicles can convey information from an area where the telecommunications network is disrupted, to an area where the telecommunications infrastructure is intact. The demonstration was a combination Vehicles as Information Hubs During Disasters: Glueing Wi-Fi to TV White Space to Cellular Networks of different technologies including Wi-Fi, TV white space, cellular networks, and the movement of the vehicles themselves. We applied and expanded Internet’s cornerstone concept of store-and-forward packet switching in a different context where the unit of “packet” was replaced with a piece of information belonging to a person, place or thing. TV white space used for V2V Vehicles as Information Hubs During Disasters: Glueing Wi-Fi to TV White Space to Cellular Networks communications in this demonstration was the first trial carried out in any metropolitan area in the world. The demonstration starts with several users in the “disaster affected” area inputting text and voice to a tablet and transmitting it to a nearby vehicle equipped with a Wi-Fi access point. Each user tablet screen was made to have a different background color so that the users would know when and how their messages move hop-by-hop in between vehicles eventually to appear in the cloud.