Machine learning (ML) can aid the challenging beam management operations in millimeter-wave (mmWave) communication systems. In this paper, we investigated the performance of four ML algorithms (i.e., K-Nearest Neighbour (KNN), Support Vector Machine (SVM), Decision Tree (DT) and Naïve Bayes (NB)) on position-aided beam prediction, using beam prediction accuracy as the metric. We used the KNN, SVM, DT, and NB algorithms to investigate the effects of beam codebook sizes and dataset sample sizes on beam prediction accuracy performance in various vehicle-to-infrastructure (V2I) scenarios using real-world datasets from extensive mmWave V2I measurements. We also illustrated the results using confusion matrices to reveal the misclassification statistics across the different beams. For the four algorithms, the results show that the larger the beam codebook size, the lower the beam prediction accuracy. The results also show that the dataset split ratio does not significantly impact the beam prediction accuracy for the four algorithms. The results point to the need for multimodal approaches that employ a combination of sensor and communication data to improve the beam prediction performance.

Physical-Layer Network Coding (PNC) is an effective technique to improve the throughput and latency in wireless networks. However, there are two major challenges for PNC, especially when using higher order modulations: 1) phase synchronization and power control at the paired User Equipments (UEs); and 2) the ambiguity removal of the PNC mapping at the relay node. To address these challenges, in this paper, we apply power control at transmitting UEs and exploit Reconfigurable Intelligent Surfaces (RISs) to synchronize the phase of the transmitted signals and ensure that they arrive at the relay with the same power and phase rotation. Then, we employ modular addition for an unambiguous PNC mapping for M-ary Quadrature Amplitude Modulations (M-QAM). We evaluate the performance of the system in the framework of Orthogonal Frequency Division Multiplexing (OFDM)-PNC for different RIS sizes and modulation orders. Furthermore, we study the sensitivity of PNC systems for Channel Estimation Error (CEE). The results reveal that 1) PNC systems show quite higher sensitivity to CEE compared with RIS-assisted one-way relay channel systems; 2) when the CEE is low, RIS can considerably enhance the Signal-to-Noise Ratio (SNR) of the PNC system, e.g., for a Bit Error Rate (BER) of 10−3(without channel coding), increasing the RIS size from one to 256 elements in 28 GHz band leads to 200% improvement in SNR.

Integrated Access and Backhaul (IAB) has been recently proposed by 3GPP to enable network operators to deploy fifth generation (5G) mobile networks with reduced costs. In this paper, we propose to use IAB to build a dynamic wireless backhaul network capable to provide additional capacity to those Base Stations (BS) experiencing congestion momentarily. As the mobile traffic demand varies across time and space, and the number of slice combinations deployed in a BS can be prohibitively high, we propose to use Deep Reinforcement Learning (DRL) to select, from a set of candidate BSs, the one that can provide backhaul capacity for each of the slices deployed in a congested BS. Our results show that a Double Deep Q-Network (DDQN) agent using a fully connected neural network and the Rectified Linear Unit (ReLU) activation function with only one hidden layer is capable to perform the BS selection task successfully, without any failure during the test phase, after being trained for around 20 episodes.

Mobile user authentication is the primary means of verifying the claimed identity of a user before granting access to resources on a mobile device. Common user authentication methods include passwords and biometrics. Despite the fact that passwords have been the most popular user authentication method for several decades, recent research suggests that they are no longer secure or convenient for mobile users due to several limitations that compromise both device security and usability. Biometric-based user authentication, on the other hand, is gaining popularity because it appears to strike a balance between security and usability. Such methods rely on human physical traits (physiological biometrics) or user involuntary actions (behavioral biometrics) for authentication. Risk-based user authentication using behavioral biometrics is particularly promising for mobile user authentication enhancing mobile authentication security while maintaining usability. In this context, we present an overview of mobile user authentication and discuss risk-based user authentication for mobile devices as a suitable approach to deal with the security vs. usability challenge. Afterwards, we test and evaluate a set of outlier detection algorithms for risk estimation in order to identify the most suitable ones for risk-based user authentication on mobile devices in terms of their accuracy and efficiency.

Physical-layer Network Coding (PNC) has great potential to improve the throughput and latency in wireless networks. However, there are two main challenges in PNC systems that do not exist in the conventional Point-to-Point$(\mathrm{P}2\mathrm{P})$communication systems: 1) time and frequency asynchrony of the paired PNC users; and 2) ambiguity of the PNC mapping at the relay node. To address these challenges, in this paper, we apply precoding for power control and phase synchronization of the paired PNC users, while we use modulo$-\sqrt{M}$addition for the PNC mapping ambiguity removal in higher-order M-ary Quadrature Amplitude Modulations (M-QAM). We evaluate the performance of the system in the framework of the Orthogonal Frequency Division Multiplexing (OFDM)-PNC systems with cyclic prefix extension under Rayleigh faded Tapped Delay Line (TDL)-C and Rician faded TDL-D channel models, proposed by the Third Generation Partnership Project$(3\text{GPP})$, as well as the Additive White Gaussian Noise (AWGN) channel model. The results reveal that our proposed technique can achieve a significant Signal-to-Noise Ratio (SNR) improvement of 12$\text{dB}$over its asynchronous OFDM-PNC counterpart (without precoding) for Binary Phase Shift Modulation (BPSK) under a TDL-C faded channel model. Moreover, without channel coding, our proposed PNC technique requires an SNR of around$13\text{dB}$to deliver a two-way 16-QAM communication at a Bit Error Rate of 10−3under a Rayleigh faded TDL-C channel.

Age of information (AoI) is an important metric of information timeliness, which determines digital twin (DT) consistency and energy management precision. However, AoI guarantee in the time-averaged sense is unreliable to avoid the occurrence of extreme event. In this paper, we propose a novel information timeliness metric named ultra-low AoI (ULAoI). Compared with AoI, ULAoI further considers the occurrence of extreme event and higher-order statistical characteristics of excess AoI value. Multi-dimensional resources of power internet of things (PIoT) are jointly allocated to achieve ULAoI guarantee from the perspective of sensing-communication-control integration. ULAoI-DT-Prioritized deep Q network (DQN) is proposed to achieve coordinated resource allocation by approximating unobservable information with the assistance of ULAoI-DT, and preventing DQN training from using samples with large AoI based on ULAoI-induced priority. Simulation results demonstrate the superior performance of the proposed algorithm in global loss function, ULAoI guarantee, and energy management optimality.

The increasing concern for identity confidentiality in the Smart City scenario has fostered research on privacy-preserving authentication based on pseudonymization. Pseudonym systems enable citizens to generate pseudo-identities and establish unlinkable anonymous accounts in cloud service providers. The citizen's identity is concealed, and his/her different anonymous accounts cannot be linked to each other. Unfortunately, current pseudonym systems require a trusted certification authority (CA) to issue the cryptographic components (e.g., credentials, secret keys, or pseudonyms) to citizens. This CA, generally a Smart City governmental entity, has the capability to grant or revoke privacy rights at will, hence posing a serious threat in case of corruption. Additionally, if the pseudonym system enables de-anonymization of misusers, a corrupted CA can jeopardize the citizens’ privacy. This paper presents a novel approach to construct a pseudonym system without a trusted issuer. The CA is emulated by a set of Smart City service providers by means of secure multi-party computation (MPC), which circumvents the requirement of assuming an honest CA. The paper provides a full description of the system, which integrates an MPC protocol and a pseudonym-based signature scheme. The system has been implemented and tested.

Attribute-based authentication is considered a cornerstone component to achieve scalable fine-grained access control in the fast growing market of cloud-based services. Unfortunately, it also poses a privacy concern. User’s attributes should not be linked to the users’ identity and spread across different organizations. To tackle this issue, several solutions have been proposed such as Privacy Attribute-Based Credentials (Privacy-ABCs), which support pseudonym-based authentication with embedded attributes. Privacy-ABCs allow users to establish anonymous accounts with service providers while hiding the identity of the user under a pseudonym. However, Privacy-ABCs require the selective disclosure of the attribute values towards service providers. Other schemes such as Attribute-Based Signatures (ABS) and mesh signatures do not require the disclosure of attributes; unfortunately, these schemes do not cater for pseudonym generation in their construction, and hence cannot be used to establish anonymous accounts. In this article, we propose a pseudonym-based signature scheme that enables unlinkable pseudonym self-generation with embedded attributes, similarly to Privacy-ABCs, and integrates a secret sharing scheme in a similar fashion to ABS and mesh signature schemes for attribute verification. Our proposed scheme also provides verifiable delegation, enabling users to share attributes according to the service providers’ policies.

Reliable and flexible emergency communication is a crucial challenge for search and rescue in the circumstance of disasters, specifically for the situation when base stations (BS) are no longer functioning. Unmanned aerial vehicle (UAV)-aided networking is becoming a prominent solution to establish emergency networks, which also should be energy-efficient. The Terahertz (THz) frequency-enabled communication system is also envisioned as enabling technology for Sixth-Generation (6G) communication systems. To this end, we consider a THz UAV network for our investigation. In this article, we study energy-efficiency (EE) analysis for UAV-Aided Terahertz-Enabled 6G Networks. The metric coverage probability (CP) is adopted as EE analysis given the QoS requirement. Using stochastic geometry tools, we derive a CP. Afterward, considering that CP, we study a network EE optimization method. Borrowing the idea from the existing literature, we reformulate the actual EE optimization problem into a solvable Dinkelbach nonlinear fractional programming. However, the optimization problem turns into a nondeterministic polynomial hard (NP-hard) problem. Eventually, we transform that NP-hard problem into a convex optimization problem using the ConCave-Convex procedure (CCCP), which is more tractable and feasible to find a solution for the optimization.

Intelligent Transportation Systems (ITS), one aspect of the Smart City paradigm, aim to improve the efficiency, convenience, and safety of travelers. The integration of (vehicular) communication technologies allows communication between the on-board communication units (OBUs) of vehicles, roadside units (RSUs), and vulnerable road users (VRUs), and contribute to the efficacy of ITS applications. However, these additional sources of information must be reliable and accurate. Security primitives such as confidentiality, integrity, and authenticity are required, but only achievable when supported with a suitable cryptographic key management scheme. This paper presents the design of a decentralized and efficient key management scheme, abbreviated as the DECENT scheme. This scheme provides secure multi-hop communication in dense and dynamic network environments while functioning in a self-organized manner. Through threshold secret sharing techniques, network nodes act as a distributed trusted third party (TTP) such that a threshold number of nodes can collaborate to execute key management functions. These functions include decentralized node admission and key updating. Novelties include (i) the unique self-healing characteristic, meaning that DECENT is capable of independently recovering from network compromise, and (ii) guidelines for choosing an appropriate security threshold in any deployment scenario which maximizes the level of security while simultaneously guaranteeing that decentralized key management services can be provided.

This paper investigates the outage probability of a vehicle-to-vehicle (V2V) multiple antenna communication link with respect to the density of antennas. The objective is to study the trade-off between complexity (number of antennas) and link performance considering explicit ground reflections. The antennas are assumed to be located at regularly distributed positions across the surface of contiguous vehicles. Under the assumption of symbol repetition across Tx antennas, we also focus on well-known mechanisms such as maximum-ratio and equal-gain combining (MRC and EGC, respectively). The objective is to minimize the outage probability of the V2V link with deterministic and stochastic channel components (i.e., Rice-distributed), where the line-of-sight (LOS) is affected by ground reflections (as an extension of the two-ray model). This scenario is more realistic for V2V applications due to the proximity of the antennas to the ground. The outage probability is averaged over a range of inter-vehicle distances with respect to the free-space loss solution. The results show that performance is improved even for a relatively small number of antennas, and that a point is reached beyond which improvement becomes differential. This suggests an optimum trade-off between outage probability and complexity can be reached over a range of inter-vehicle distances.

User authentication acts as the first line of defense verifying the identity of a mobile user, often as a prerequisite to allow access to resources in a mobile device. For several decades, user authentication was based on the “something the user knows”, known also as knowledge-based user authentication. Recent studies state that although knowledge-based user authentication has been the most popular for authenticating an individual, nowadays it is no more considered secure and convenient for the mobile user as it is imposing several limitations. These limitations stress the need for the development and implementation of more secure and usable user authentication methods. Toward this direction, user authentication based on the “something the user is” has caught the attention. This category includes authentication methods which make use of human physical characteristics (also referred to as physiological biometrics), or involuntary actions (also referred to as behavioral biometrics). In particular, risk-based user authentication based on behavioral biometrics appears to have the potential to increase mobile authentication security without sacrificing usability. In this context, we, firstly, present an overview of user authentication on mobile devices and discuss risk-based user authentication for mobile devices as a suitable approach to deal with the security vs. usability challenge. Afterwards, a set of novelty detection algorithms for risk estimation is tested and evaluated to identify the most appropriate ones for risk-based user authentication on mobile devices.

Communications, wired and wireless, have integrated various cryptographic techniques to ensure privacy and counter surveillance. These techniques have been integrated in most of the network layers, except for the physical layer. This physical layer has, thus far, dealt with schemes such as source coding, channel coding, and (de)modulation, to enable the transmission of data in a reliable and efficient manner. The emergence of physical layer security extends the functionalities at the physical layer to include secure communication, aiming at the transmission of a signal that can only be correctly retrieved by the intended receiver. Therefore, the goals of physical layer security align with cryptographic schemes utilized at the other network layers. From the extensive study of physical layer security schemes, we have observed that there is a knowledge gap regarding certain security principles practiced by cryptographers and the experts within physical layer security, causing many physical layer security schemes to be impractical for standardization and the wide-scale integration into information and communication technologies. This paper describes a variety of security principles and concepts, practiced by cryptographers, and of importance to physical layer security experts. We aim to raise the awareness of these security principles and concepts to experts within the field of physical layer security to improve the practicality, standardization, and integration potential of the design of future physical layer security schemes.

Network virtualization allows the coexistence of multiple network slices over a shared physical infrastructure, each delivering a service with own requirements in terms of quality of service, coverage, and time span. Spectrum is an expensive commodity, so it must be managed among these slices in the most efficient way. However, current spectrum sharing techniques are too rigid to address all combinations of service requirements and properly exploit the new air interface flexibility and network deployment options introduced by the Fifth Generation (5G) mobile networks. In this paper, we extend the state of the art first by proposing a novel spectrum sharing scheme that supports an unlimited number of radio networks. Second, we propose a 5G compliant novel service-based network management architecture to integrate Machine Learning (ML) algorithms for Radio Resource Management (RRM), including spectrum sharing. Finally, we propose a new three-stage ML framework that exploits forecasting, clustering and reinforcement learning algorithms to implement the proposed spectrum sharing scheme. Our proposed solution can allow an arbitrary number of 5G network slices to share spectrum more effectively and more dynamically either with each other or with other radio networks.

The new developments in mobile edge computing (MEC) and vehicle-to-everything (V2X) communications has positioned 5G and beyond in a strong position to answer the market need towards future emerging intelligent transportation systems and smart city applications. The major attractive features of V2X communication is the inherent ability to adapt to any type of network, device, or data, and to ensure robustness, resilience and reliability of the network, which is challenging to realize. In this work, we propose to drive further these features by proposing a novel robust, resilient and reliable architecture for V2X communication based on harnessing MEC and blockchain technology. A three stage computing service is proposed. Firstly, a hierarchcial computing architecture is deployed spanning over the vehicular network that constitutes cloud computing (CC), edge computing (EC), fog computing (FC) nodes. The resources and data bases can migrate from the high capacity cloud services (furthest away from the individual node of the network) to the edge (medium) and low level fog node, according to computing service requirements. Secondly, the resource allocation filters the data according to its significance, and rank the nodes according to their usability, and selects the network technology according to their physical channel characteristics. Thirdly, we propose a blockchain-based transaction service that ensures reliability. We discussed two use cases for experimental analysis, plug-in electric vehicles in smart grid scenarios, and massive IoT data services for autonomous cars. The results show that car connectivity prediction is accurate 98% of the times, where 92% more data blocks are added using micro-blockchain solution compared to the public blockchain, where it is able to reduce the time to sign and compute the proof-of-work (PoW), and deliver a low-overhead Proof-of-Stake (PoS) consensus mechanism. This approach can be considered a strong candidate architecture for future V2X, and with more general application for everything-to-everything (X2X) communications.

Reliable and flexible emergency communication is a crucial challenge for search and rescue in the circumstance of disasters, specifically for the situation when base stations (BS) are no longer functioning. Unmanned aerial vehicle (UAV)aided networking is becoming a prominent solution to establish emergency networks with the underlay device-to-device (D2D), which also should be energy-efficient. In this article, we study energy-efficiency (EE) maximization for interference-aware underlay D2D-enabled UAV-aided 5G systems. All the interference scenarios are taken into account while modeling the system architecture. Afterward, we formulate an objective function to optimize EE maximization, which shows the characteristic of an NP-hard nonconvex research problem. Therefore, we transform the nonconvex problem into a convex one by reformulating the constraint functions with the cubic inequality method. Several criteria are developed to satisfy the non-negativity of the reformulating constraint. This leads the problem to be solved as a convex optimization method and results in an efficient iterative resource allocation algorithm. In each iteration, the transformed problem is solved by using Lagrangian dual decomposition with a projected gradient method. In the end, we analyze the convergence behavior of the studied algorithm and also compared it with another existing algorithm through numerical simulations.

To increase network capacity and coverage, new deployment strategies are being explored for beyond-5G networks. Building on the unique features and potentials of lamp post-based access points (APs) for hotspot deployment scenarios, this work investigates the performance of three hybrid precoding techniques at the APs, where the analog RF beamformer is based on orthogonal matching pursuit (OMP) and modified generalized low rank approximation of matrices (MGLRAM) while the digital baseband precoder is based on maximum ratio transmission (MRT) and zero forcing (ZF) precoding schemes. The performance of these hybrid precoding techniques is compared to the SVD-based upper bound. Using a stadium deployment as sample use case, this work considers a multi-cell, multi-user THz Massive MIMO operation for harnessing beamforming and multiplexing gains, where the APs use hybrid precoding while users employ analog-only beamforming. The spectral efficiency results show that this deployment strategy can deliver ultra-broadband connectivity for next-generation short-range communication networks.

Due to the bursty nature of Internet traffic, network service providers (NSPs) are forced to expand their network capacity in order to meet the ever-increasing peak-time traffic demand, which is however costly and inefficient. How to shift the traffic demand from peak time to off-peak time is a challenging task for NSPs. In this paper, we study the implementation of time-dependent pricing (TDP) for bandwidth slicing in software-defined cellular networks under information asymmetry and price discrimination. Congestion prices indicating real-time congestion levels of different links are used as a signal to motivate delay-tolerant users to defer their traffic demands. We formulate the joint pricing and bandwidth demand optimization problem as a two-stage Stackelberg leader-follower game. Then, we investigate how to derive the optimal solutions under the scenarios of both complete and incomplete information. We also extend the results from the simplified case of a single congested link to the more complicated case of multiple congested links, where price discrimination is employed to dynamically adjust the price of each congested link in accordance with its real-time congestion level. Simulation results demonstrate that the proposed pricing scheme achieves superior performance in increasing the NSP's revenue and reducing the peak-to-average traffic ratio (PATR).

Crowdsensing requires scalable privacy-preserving authentication that allows users to send anonymously sensing reports, while enabling eventual anonymity revocation in case of user misbehavior. Previous research efforts already provide efficient mechanisms that enable conditional privacy through pseudonym systems, either based on Public Key Infrastructure (PKI) or Group Signature (GS) schemes. However, previous schemes do not enable users to self-generate an unlimited number of pseudonyms per user to enable users to participate in diverse sensing tasks simultaneously, while preventing the users from participating in the same task under different pseudonyms, which is referred to as sybil attack. This paper addresses this issue by providing a scalable privacy-preserving authentication solution for crowdsensing, based on a novel pseudonym-based signature scheme that enables unlinkable-yet-accountable pseudonymity. The paper provides a detailed description of the proposed scheme, the security analysis, the performance evaluation, and details of how it is implemented and integrated into a real crowdsensing platform.

In this paper, we investigate a metamaterial- inspired small rectenna (rectifying antenna) system for microwave energy harvesting and wireless power transfer at 5.8 GHz. We also study the received-power maximization technique using an array of antennas connected to a single load by an optimal RF power combiner. This investigation is done both in simulation and experiment. The power combiner is embedded between the metamaterial- inspired antennas and the load (including rectifier) for maximizing the power, harvested by the input antennas and to deliver it to the load in the most optimal way. Moreover, we design and test proof-of-concept prototypes of the main components of the investigated energy harvesting systems. The similarity between the simulation and the experimental results also confirms our method of investigation.

In this paper, the 5G wireless communication system with hybrid energy supply is considered, where the energy arrival variations and the channel fading are of different time-scales. In such a system, there exists multi-dimension randomness caused by the variations of energy harvesting, channel fading and electricity price. Faced with this challenge, we address the two time-scale cross-layer resource allocation problem to maximize the user experience while minimizing the energy cost from a long-term perspective. The formulated problem can be decoupled into three subproblems based on Lyapunov optimization, including the energy management subproblem over the large time-scale, and the rate control subproblem as well as the joint channel and power allocation subproblem over the small time- scale. Next, these subproblems are solved by combining linear programming, convex optimization, and matching theory, without the requirement of noncausal information. Finally, simulation results demonstrate that the proposed algorithm can achieve superior performance while guarantee reliable data transmission and efficient energy utilization.

Spectrum use will undoubtedly move to the terahertz (THz) frequencies in the beyond fifth-generation (B5G) mobile system era. With enormous bandwidth far greater than the amount available in the microwave and millimeter-wave bands combined, THz communication will open up new frontiers for exciting services and applications requiring ultra-broadband connectivity. In this work, we evaluate the performance of a candidate B5G scenario with THz-enabled massive MIMO access points mounted on street lampposts to serve pedestrian users. Using spectral efficiency (SE) and energy efficiency (EE) as metrics, we compared the performance of three precoding schemes, namely: analog-only beamsteering, hybrid precoding with baseband zero forcing and singular value decomposition precoding as upper bound. We also show the impacts of carrier frequency, bandwidth and antenna gain on the system performance. The simulation results reveal the optimal EE and SE points which are critical design goals for the green and sustainable operation of next-generation networks.

With the wide development of smart devices, fog computing has emerged as a promising solution to accommodate the ever-increasing computational demands in Internet of things (IoT). However, there are two major obstacles hindering the wide deployment of IoT fog computing, i.e., how to realize server recruitment under information asymmetry and reliable task assignment under information uncertainty. In this article, we develop a robust two-stage task offloading algorithm by integrating contract theory with computational intelligence. In the first stage, we propose a contract based server recruitment scheme to motivate servers to share residual computational resources. In the second stage, by leveraging multi-armed bandit (MAB), we develop a reliable volatile upper confidence bound (RV-UCB) algorithm to minimize the long-term delay of task assignment, which takes into account task awareness, occurrence awareness and location awareness. Finally, a series of stimulation results are carried out to validate the performance of the proposed algorithm.

The advances and fast spread of mobile devices and technologies, we witness today, have extended its advantages over medical and health practice supported by mobile devices, giving rise to the growing research of Internet of Things (IoT), especially the e-Health field. The features provided by mobile technologies revealed to be of major importance when we consider the continuous aging of population and the consequent increase of its debilities. In addition to the increase of lifetime span of population, also the increase of health risks and their locomotive impairments increases, requiring a close monitoring and continuous evaluation. Such monitoring should be as noninvasive as possible, in order not to compromise the mobility and the day-to-day activities of citizens. Therefore, we present the development of a noninvasive optical fiber sensor (OFS) architecture adaptable to a shoe sole for plantar pressure remote monitoring, which is suitable to be integrated in an IoT e-Health solution to monitor the well-being of individuals. This paper explores the production of the OFS multiplexed network (using fiber Bragg gratings) to monitor the foot plantar pressure distribution during gait (walking movement). From the acquired gait data, it is possible to infer health conditions of the patient's foot and spine posture. To guarantee the patients mobility, the proposed system consists of an OFS network integrated with a wireless transceiver to enable efficient ubiquitous monitoring of patients. This paper shows the calibration and measurement results, which reflect the accuracy of the proposed system, under normal walking in controlled area.

In the emerging industrial Internet of Things (IIoT) era, machine-to-machine (M2M) communication technology is considered as a key underlying technology for building IIoT environments, where devices (e.g., sensors, actuators, and gateways) are enabled to exchange information with each other in an autonomous way without human intervention. However, most of the existing M2M protocols that can be also used in the IIoT domain provide security mechanisms based on asymmetric cryptography resulting in high computational cost. As a consequence, the resource-constrained IoT devices are not able to support them appropriately and thus, many security issues arise for the IIoT environment. Therefore, lightweight security mechanisms are required for M2M communications in IIoT in order to reach its full potential. As a step toward this direction, in this paper, we propose a lightweight authentication mechanism, based only on hash and XOR operations, for M2M communications in IIoT environment. The proposed mechanism is characterized by low computational cost, communication, and storage overhead, while achieving mutual authentication, session key agreement, device's identity confidentiality, and resistance against the following attacks: replay attack, man-in-the-middle attack, impersonation attack, and modification attack.

Nowadays, the serious security threat of industrial control system and sensors has become a major challenge with the rapid development of Industrial Internet of Things (IIoT). Man-in-the-middle (MITM) attack is a very common intrusion method, which will make a great security threat in the application of IIoT. In IIoT scenario, the lightweight safety certification can play a very important role in the development of data-intensive and decentralized applications running on billions of sensors and devices, preserving their security. Therefore, in this paper, a low-latency high-reliability security mechanism is proposed to avoid the MITM attack in IIoT scenario. First, combining the radio frequency fingerprint (RFF) technology with IIoT applications, a lightweight IIoT security architecture is proposed. Based on the proposed IIoT security architecture, the process of device access authentication and communication service is illustrated. Second, according to the requirement of IIoT identification method, an access authentication method of the device is proposed based on the RFF. The method of feature extraction, classifier designing, and the access authentication process is discussed in detail. Finally, the simulation results show that the identification rate of devices can reach 95% under SNR = 6 dB, and can nearly reach 100% under SNR = 15 dB. Through the new process of access authentication, the access authentication rate can reach 95% under SNR = 15 dB. Therefore, according to the simulation results, the new security mechanisms based on RFF can be used to avoid the MITM attack in IIoT scenario.

Millimeter-wave (mm-wave) communication has attracted increasing attention as a promising technology for 5G networks. One of the key architectural features of mm-wave is the possibility of using large antenna arrays at both the transmitter and receiver sides. Therefore, by employing directional beamforming, both mm-wave base stations (MBSs) and mm-wave user equipments (MUEs) are capable of supporting multi-beam simultaneous transmissions. However, most of the existing research results have only considered a single beam. Thus, the potentials of mm-wave have not been fully exploited yet. In this context, in order to improve the performance of short-range indoor mm-wave networks with multiple reflections, we investigate the challenges and potential solutions of downlink multi-user multi-beam transmission, which can be described as a beamspace multi-user multiple-input multiple-output (MU-MIMO) technique. We first exploit the characteristic of MBS/MUEs supporting multiple beams simultaneously to improve the efficiency of multi-user BF training. Then, we analyze the inter-user interference to avoid beam selection conflicts. Furthermore, we propose blockage control strategies and multi-user multi-beam power allocation solutions for the beamspace MU-MIMO. The theoretical and numerical results demonstrate that the beamspace MU-MIMO compared with single beam transmission can largely improve the rate performance and robustness of mm-wave networks.

Network coding (NC)-enabled mobile small cells are observed as a promising technology for fifth-generation (5G) networks that can cover the urban landscape by being set up on-demand at any place and at any time on any device. Nevertheless, despite the significant benefits that this technology brings to the 5G of mobile networks, major security issues arise due to the fact that NC-enabled mobile small cells are susceptible to pollution attacks; a severe security threat exploiting the inherent vulnerabilities of NC. Therefore, intrusion detection and prevention mechanisms to detect and mitigate pollution attacks are of utmost importance so that NC-enabled mobile small cells can reach their full potential. Thus, in this article, we propose for the first time, to the best of our knowledge, a novel intrusion detection and prevention scheme (IDPS) for NC-enabled mobile small cells. The proposed scheme is based on a null space-based homomorphic message authentication code (MAC) scheme that allows detection of pollution attacks and takes proper risk mitigation actions when an intrusive incident is detected. The proposed scheme has been implemented in Kodo and its performance has been evaluated in terms of computational overhead.

Inspired by the increasingly mature vehicle-to-everything (V2X) communication technology, we propose a multihop V2X downlink transmission system to improve users’ quality of experience (QoE) in hot spots. Specifically, we develop a cross-layer resource allocation algorithm to optimize the long-term system performance while guaranteeing the stability of data queues. Lyapunov optimization is employed to transform the long-term optimization problem into a series of instantaneous subproblems, which involves the joint optimization of rate control, power allocation, and mobile relay selection at each time slot. On one hand, the optimization of rate control is decoupled and carried out independently. On the other hand, a low-complexity pricing-based stable matching algorithm is proposed to solve the joint power allocation and mobile relay selection problem. Finally, simulation results demonstrate that the proposed algorithm can achieve superior performance and simultaneously guarantee queue stability.

Massive, green, soft and super-fast are the key attributes that will describe next-generation mobile networks (5G and beyond). Large millimeter-wave (mmWave) bandwidths, massive MIMO antenna arrays, ultra-dense small cells (UDN) and cloud radio access network (C-RAN), among others, will enable these future networks to deliver huge performance gains. Since the networks are anticipated to be green and the available spectrum will be more abundant, energy efficiency (EE) becomes a more critical design factor than spectral efficiency (SE). In the face of competing potentials and challenges brought about by the different enablers, efficient resource allocation (RA) schemes are important to optimize system performance. In this work, we propose a fully-adaptive RA for a dense, C-RAN-enabled mmWave massive MIMO network. We then compare its performance to a non-adaptive and two semi-adaptive RA schemes. The fully-adaptive scheme outperforms all the other RA schemes and shows promising potentials for the joint EE-SE optimization of future mobile networks. Our results show the optimal EE-SE points and the impact of the transmit power and the number of data streams on the EE and SE performance.

The continuous aging of the population has increased health risks, mostly translated in mobility impairments. New therapeutic options have come to light in the past years, with the rise of e-health architectures. The use of optical fiber based sensors has been considered as a possible e-health enabling technology, due to their advantages compared to electronic based sensors. In this paper, we present an e-health optical fiber based solution to monitor the knee angular flexion/extension during gait. The presented design is an accurate non-invasive solution, using kinesio tape and optical fiber Bragg grating sensors, in order not to limit the movement of the individuals. The paper shows the accuracy of the acquired measurements during gait using the proposed designed sensor.

Internet of Things (IoT) and cloud computing are steadily growing in support of recent and projected revolutionary Internet applications. Cloud computing has the ability to meet the performance expectations of different applications. In this paper, we present the implementation of applications based on cooperative resource and energy-constrained objects with optimized performances. To dynamically incorporate objects into IoT applications' execution, task scheduling should be implemented for resource allocation in an optimized manner. We propose a task scheduling algorithm based on robust canonical particle swarm optimization (CPSO) and fully-informed particle swarm (FIPS) algorithms in order to solve the problem of resource allocation and management in both homogeneous and heterogeneous IoT cloud computing. Our objective is to satisfy the Quality of Service (QoS) in terms of throughput and delay, by performing optimal task scheduling taking into consideration different classes of data traffic. Performance evaluation of experiments show that throughput and delay can be significantly improved by dynamic dedicated servers (DDSS) and heterogeneous DDSS (h-DDSS) using FIPS optimization algorithm compared to CPSO optimization algorithm.

The emerging vehicular ad-hoc networks (VANETs) have paved the way to Cooperative Adaptive Cruise Control (CACC) applications. With such application, cars can travel in platoons with very small headways and thus achieve considerable capacity and fuel consumption gains. In order to ensure safety while exploiting such gains, intra/inter-platoon communications have to rely on a fast and reliable information exchange. Thus, it is important to define relaying schemes which can ensure that reliable platoon applications can be run over the unreliable wireless channel. In this paper, we propose a new dissemination algorithm for the Decentralized Environmental Notification Message (DENMs) over platoons. Our dissemination algorithm is responsible for electing the vehicles among platoon members for relaying the DENMs. The reliability is met by a proper selection of the best relay based on bidirectional link quality and distance criteria. Unlike existing method for the estimation of the bidirectional link quality (BDSC), which is more suitable for unicast context, we propose an algorithm that accounts for the dissemination context in platoons. A performance comparison with four state-of-the-art relaying approaches, based on simulations, shows the high performance of our approach in terms of end to end delay, reachability, and average number of hops.

LoRaWAN is a Low Power Wide Area Network (LPWAN) protocol designed to allow low power battery operated nodes to communicate with each other. Though LoRaWAN provides end-to-end security, however vulnerabilities exist in the security mechanism of LoRaWAN join procedure. A jammer can be used to launch a denial of service (DOS) attack by permanently disconnecting the LoRa end nodes from the LoRaWAN network. In this paper, we propose a novel LoRaWAN based Intrusion Detection System (LIDS) for jamming attacks. A real experimental testbed is developed and deployed and LIDS is trained on real join request data. We propose two LIDS algorithms based on Kullback Leibler Divergence (KLD) and Hamming distance (HD). The algorithms are extensively tested on realworld dataset. Receiver Operating Characteristic (ROC) based performance evaluations show that KLD and HD can achieve detection rates as high as 98% and 88% respectively with 5% false positive rate.

In this paper, we investigate how to achieve reliable content distribution in device-to-device (D2D) based cooperative vehicular networks by combining big data based vehicle trajectory prediction with coalition formation game based resource allocation. Firstly, vehicle trajectory is predicted based on global positioning system (GPS) and geographic information system (GIS) data, which is critical for finding reliable and longlasting vehicle connections. Then, the determination of content distribution groups with different lifetimes is formulated as a coalition formation game. We model the utility function based on the minimization of average network delay to guarantee the end-to-end quality of service (QoS), which is transferable to the individual payoff of each coalition member according to its contribution. The merge and split process is implemented iteratively based on preference relations, and the final partition is proved to converge to a Nash- stable equilibrium. Finally, we evaluate the proposed algorithm based on real-world map and realistic vehicular traffic.

This paper investigates the optimal number of antennas at a base station, in contrast to what has been accepted in the past: that increasing the number of antennas at base station always enhances performance. In this study, we show that increasing the number of antennas does not always improve the desired performance. Additionally, such increase in antennas consumes more power in transmission and adds to the computation complexity, which in turn needs more time and is more difficult to implement. The optimum number of antennas has been evaluated using simulations. The simulation results show that the optimal ratio equals to 1.2 times the number of active users in each time frame.

Coordinated vehicles travelling in platoons can improve road capacity, reduce fuel consumption and offer a more comfortable travelling experience for the driver. However, for platooning to achieve such gains, headway distances between travelling cars have to be kept small even at high speeds. The feasibility of such platooning approach, hence relies entirely on the availability of fast and reliable information exchange between vehicles, which occurs over an unreliable wireless communication channel. In this paper, we analyse the dissemination of safety related messages in platoon-based VANETs. We propose an enhancement of the reliability of the dissemination algorithm by modifying the relay election procedure to include the bidirectional link quality in addition to distance criteria. A performance comparison is presented using simulations, which shows that our proposed enhancement provides comparable end-to-end delay with higher reliability for DENMs dissemination over platoons.

In this paper, we investigate how to apply industrial unmanned aerial vehicles (UAVs) for autonomous power line inspection in smart grid from an energy efficiency perspective. Firstly, the energy consumption minimization problem is formulated as a joint optimization problem, which involves both the large-timescale optimization and the small-timescale optimization. Then, the NP-hard joint optimization problem is transformed to a two- stage optimization problem based on energy consumption magnitude and optimization timescale differences. Next, the first-stage and second-stage problems are solved by exploring dynamic programming (DP) and auction matching, respectively. Finally, the proposed algorithm is verified based on realistic power grid topology. Simulation results demonstrate that the proposed scheme achieves significant energy consumption reduction.

In the forthcoming Smart City scenario, Service Providers will require users to authenticate themselves and authorize their mobile applications to access their remote accounts. In this scenario, OAuth 2.0 has been widely adopted as a de facto authentication and authorization protocol. However, the current OAuth 2.0 protocol specification does not consider the user privacy issue and presents several vulnerabilities that can jeopardize users’ privacy rights. Therefore, in this paper we propose an OAuth 2.0 based protocol for Smart City mobile applications that addresses the user privacy issue by integrating a pseudonym-based signature scheme and a signature delegation scheme into the OAuth 2.0 protocol flow. The proposed solution allows users to self-generate user-specific and app-specific pseudonyms on-demand and ensure privacy-enhanced user authentication at the Service Provider side. The proposed protocol has been validated with Proverif and its performance has been evaluated in terms of time and space complexity. Results show that the proposed protocol can provide users with efficient and effective means to authenticate towards service providers while preventing user tracking and impersonation from malicious entities located in the network side or in the users’ mobile device.

Exploiting physical layer in achieving different security aspects in wireless communications has been widely encouraged. In this work, the authors propose an entity authentication scheme for mobile devices with multiple antennas, which is purely based on physical layer parameters. According to the proposed scheme, in order to authenticate a device, a number of predefined authentication signals should be detected at the receive antennas on the authenticator side. The transmitted signals are designed based on the instantaneous channel responses in order to deliver the authentication signals to the receiver. The proposed scheme works efficiently even for mobile users, which is considered a significant improvement over previous related works. Mathematical analysis of the different involved factors along with sufficient simulations show the high performance of the proposed authentication scheme.

With the increasing popularity of unmanned aerial vehicles (UAVs), it is foreseen that they will play an important role in broadening the horizon of mobile crowd sensing (MCS). Specifically, UAV-aided MCS allows autonomous data collection anytime and anywhere due to the capability of fast deployment and controllable mobility. However, the on-board battery capacity of UAVs imposes a limitation on their endurance capability and performance. In this paper, we consider the fixed-wing UAV-aided MCS system and investigate the corresponding joint route planning and task assignment problem from an energy efficiency perspective. The formulated joint optimization problem is transformed into a two-sided two-stage matching problem, in which the route planning problem is solved in the first stage based on either dynamic programming or genetic algorithms, and the task assignment problem is addressed in the second stage by exploring the Gale-Shapley algorithm. We provide a comprehensive theoretical analysis, and elaborate the procedures of practical implementation. Numerical results demonstrate that significant performance improvement can be achieved by the proposed scheme.

The large amount of data among billions of devices deployed for Industrial Internet of Things (IIoT) cause a massive energy consumption. Driven by the pursuit of green communication, this paper presents a space-reserved cooperative caching scheme for IIoT in the fifth generation mobile heterogeneous networks, where the cache space in a base station is divided into two parts, one is used to store the prefetched data from the servers ahead of the device request time and the other is reserved to store the temporarily buffering data in the wireless transmission queue at the device request time. With the constraint that the quality of service is guaranteed, we propose an algorithm to obtain the optimal proportion between the two parts of the cache space for the purpose of reducing the average energy consumption. Simulation results verified that the proposed caching scheme is more efficient than the conventional one with respect to the average energy consumption.

By analogy with Internet of things, Internet of vehicles (IoV) that enables ubiquitous information exchange and content sharing among vehicles with little or no human intervention is a key enabler for the intelligent transportation industry. In this paper, we study how to combine both the physical and social layer information for realizing rapid content dissemination in device-to-device vehicle-to-vehicle (D2D-V2V)-based IoV networks. In the physical layer, headway distance of vehicles is modeled as a Wiener process, and the connection probability of D2D-V2V links is estimated by employing the Kolmogorov equation. In the social layer, the social relationship tightness that represents content selection similarities is obtained by Bayesian nonparametric learning based on real-world social big data, which are collected from the largest Chinese microblogging service Sina Weibo and the largest Chinese video-sharing site Youku. Then, a price-rising-based iterative matching algorithm is proposed to solve the formulated joint peer discovery, power control, and channel selection problem under various quality-of-service requirements. Finally, numerical results demonstrate the effectiveness and superiority of the proposed algorithm from the perspectives of weighted sum rate and matching satisfaction gains.

Driven by the evolutionary development of automobile industry and cellular technologies, dependable vehicular connectivity has become essential to realize future intelligent transportation systems (ITS). In this paper, we investigate how to achieve dependable content distribution in device-to-device (D2D)-based cooperative vehicular networks by combining big data-based vehicle trajectory prediction with coalition formation game-based resource allocation. First, vehicle trajectory is predicted based on global positioning system and geographic information system data, which is critical for finding reliable and long-lasting vehicle connections. Then, the determination of content distribution groups with different lifetimes is formulated as a coalition formation game. We model the utility function based on the minimization of average network delay, which is transferable to the individual payoff of each coalition member according to its contribution. The merge and split process is implemented iteratively based on preference relations, and the final partition is proved to converge to a Nash-stable equilibrium. Finally, we evaluate the proposed algorithm based on real-world map and realistic vehicular traffic. Numerical results demonstrate that the proposed algorithm can achieve superior performance in terms of average network delay and content distribution efficiency compared with the other heuristic schemes.

Cellular networks with hyper-dense deployment of small cells have been identified as the performance-optimizing architecture for fifth generation (5G) mobile systems. A thousand-fold capacity increase is projected when such networks benefit from aggressive spatial multiplexing and huge bandwidth, realizable with massive antenna arrays and millimeter-wave (mmWave) spectrum, respectively. As a precursor to the 5G projections, we show in this paper that network performance (cell capacity, user throughputs and spectral efficiency) can be significantly increased by overlaying mmWave small cells on microwave (μWave) macrocells, due to the elimination of cross-tier interference. This is without any increase in bandwidth or antenna configuration of legacy dense networks. Dramatic performance gains can further be achieved by employing more antenna arrays and larger bandwidth. In addition, we demonstrate that such networks are density-limited. Increasing the number of small cells per macrocell beyond the optimal cell density threshold leads to performance degradation. Adequate consideration should, therefore, be given to this limit in the design, planning and operation of future cellular networks.

The continuous aging of the population has increased health risks, leading to the need for close monitoring of elder citizens and patients. Fortunately, the field of e-Health has been experiencing great progress, although it still faces challenges, since it needs to be applied without compromising the mobility or lifestyle of monitored patients; i.e. non-invasive. Addressing such challenges, we present a non-invasive energy- efficient insole optical fiber sensor architecture for plantar pressure monitoring. The paper shows the design and implementation of a non-invasive "in-sole" optical fiber sensor network (using Fiber Bragg Gratings), which is able to monitor health conditions by observing plantar pressure distribution in the foot. The system integrates the optical fiber sensor network with an energy efficient wireless transceiver, in order to allow continuous monitoring of monitored patients, during their uninterrupted daily routine. The paper presents the calibration and laboratory measurements of the sensing network. The recorded measurements show the suitability and high accuracy of the proposed system in monitoring the plantar pressure distribution during walking movement (i.e. gait).

In this paper, we present an Internet of Things (IoT)-based health care system implementation scheme using Hidden Markov Model (HMM) chain and ElectroCardioGram (ECG) sensors within the context of e-Health. The scheme aims to facilitate improved monitoring and timely intervention for Cardio Vascular Diseases (CVD) patients thereby enhancing medical services for such patients. As real-time monitoring of patients from different locations remains a critical challenge for IoT-based health care systems, this implementation employs patient path estimator, patient table and alert management schemes within the hospital to facilitate the localisation and timely intervention for the treatment of CVD patients.

Mobile devices are used for communication and for tasks that are sensitive and subject to tampering. Indeed, attacks can be performed on the users' devices without user awareness, this represents additional risk in mission critical scenarios, such as Public Protection and Disaster Relief (PPDR). Intrusion Detection Systems are important for scenarios where information leakage is of crucial importance, since they allow to detect possible attacks to information assets (e.g., installation of malware), or can even compromise the security of PPDR personnel. HyIDS is an Hybrid IDS for Android and supporting the stringent security requirements of PPDR, by comprising agents that continuously monitor mobile device and periodically transmit the data to an analysis framework at the Command Control Center (CCC). The data collection retrieves resource usage metrics for each installed application such as CPU, memory usage, and incoming and outgoing network traffic. At the CCC, the HyIDS employs Machine Learning techniques to identify patterns that are consistent with malware signatures based on the data collected from the applications. The HyIDS's evaluation results demonstrate that the proposed solution has low impact on the mobile device in terms of battery consumption and CPU/memory usage.

An Intrusion Detection System (IDS) aims at protecting a network against attacks intended to exposing and/or vandalizing it. To build and test an IDS, network data are usually acquired containing attacks and normal behavior. The objective of this work is to use machine learning techniques to build IDSs and to investigate their reliability. To build and test the IDSs, KDDCUP99 has been used. The data contain a training set and a testing set with 4,898,430 samples (∼700MB) and 311,032 samples (∼45MB), respectively. However, the cleaned dataset via using SQL commands show that KDDCUP99 is highly redundant. The cleaned/distinct data are nearly one fifth of the original. Subsequently, experimental results have been performed using neural networks based IDSs. Some IDSs give low and median performances when tested using the redundant data and the distinct data, respectively, but other IDSs gave high and median performances using the redundant and the distinct data, respectively. Thus, there is a fluctuation in the performance when the data are redundant, which shows that an IDS built using a redundant dataset has unstable performance. The goal of preparing a balanced dataset is to only use it in testing the realistic performance of the IDS and has no relation to IDS generalization and implementation in real-world scenarios.

Smart grid introduces a wealth of promising applications for upcoming fifth-generation mobile networks (5G), enabling households and utility companies to establish a two-way digital communications dialogue, which can benefit both of them. The utility can monitor real-time consumption of end users and take proper measures (e.g., real-time pricing) to shape their consumption profile or to plan enough supply to meet the foreseen demand. On the other hand, a smart home can receive real-time electricity prices and adjust its consumption to minimize its daily electricity expenditure, while meeting the energy need and the satisfaction level of the dwellers. Smart Home applications for smart phones are also a promising use case, where users can remotely control their appliances, while they are away at work or on their ways home. Although these emerging services can evidently boost the efficiency of the market and the satisfaction of the consumers, they may also introduce new attack surfaces making the grid vulnerable to financial losses or even physical damages. In this paper, we propose an architecture to secure smart grid communications incorporating an intrusion detection system, composed of distributed components collaborating with each other to detect price integrity or load alteration attacks in different segments of an advanced metering infrastructure.

Non-orthogonal multiple access (NOMA), millimeter wave (mmWave), and massive multiple-input-multiple-output (MIMO) have been emerging as key technologies for fifth generation mobile communications. However, less studies have been done on combining the three technologies into the converged systems. In addition, how many capacity improvements can be achieved via this combination remains unclear. In this paper, we provide an in-depth capacity analysis for the integrated NOMA-mmWave-massive-MIMO systems. First, a simplified mmWave channel model is introduced by extending the uniform random single-path model with angle of arrival. Afterward, we divide the capacity analysis into the low signal to noise ratio (SNR) and high-SNR regimes based on the dominant factors of signal to interference plus noise ratio. In the noise-dominated low-SNR regime, the capacity analysis is derived by the deterministic equivalent method with the Stieltjes–Shannon transform. In contrast, the statistic and eigenvalue distribution tools are invoked for the capacity analysis in the interference-dominated high-SNR regime. The exact capacity expression and the low-complexity asymptotic capacity expression are derived based on the probability distribution function of the channel eigenvalue. Finally, simulation results validate the theoretical analysis and demonstrate that significant capacity improvements can be achieved by the integrated NOMA-mmWave-massive-MIMO systems.

Device-to-device (D2D) communication can effectively meet the demanding high data rate by providing direct links among mobile users in cellular networks. In this paper, we analyze the transmission capacity of relay-assisted D2D communication coexisting with cellular networks in both overlay and underlay modes. D2D users can use the delicate spectrum resources in the overlay mode, while reuse the cellular resources in the underlay mode. Based on stochastic geometry, cellular users, D2D transmitters, and relay nodes (RNs) in the networks are all modeled as Poisson point process. Then, we calculate the RN existence probability and the expectation of relay link distance to obtain the successful transmission probabilities for D2D communication. According to two relay mechanisms for enhancing D2D transmission distance, we further obtain the transmission capacities of D2D communication with the assistance of RNs in both two modes, which reflect the influence from D2D density and power. In addition, the D2D transmission capacities with variable D2D link distance are also analyzed in two modes. Simulation results verify that D2D transmission capacity can be enhanced by relay transmission and influenced by a multitude of factors, including the user density, power, the D2D link distance, and the way of using RNs.

Device-to-Device (D2D) communication capability within a cellular network is an integral part of the LTE-Advanced standards since 3GPP release 12. D2D communication is being targeted for traffic loading applications and proximity based services, and has the potential to enhance spectrum efficiency, overall system throughput and energy efficiency, whilst reducing the system communication delay. However, interference is still a key challenge in D2D-based communications. This will require an analytical toolkit that measure and evaluate the impact of interference on system performance, as well suggest guidelines for operators towards proper network planning. In this paper, we investigate outage probability (OP) as a key performance indicator, considering the signal-to-interference-plus-noise ratio (SINR) threshold for a D2D system. In this context, we exploit stochastic geometry, Laplace transforms, and probability density function (pdf) to calculate the outage probability in a tractable manner. The results of the mathematical analysis are verified through computer simulation.

In the context of the Smart City concept, mobile e-Health applications can play a pivotal role towards the improvement of citizens' quality of life, since they can enable citizens to access personalized e-Health services, without limitations on time and location. However, accessing personalized e-Health services through citizens' mobile e-Health applications, running on their mobile devices, raises many privacy issues in terms of citizens' identity and location. These privacy issues should be addressed so that citizens, concerned about privacy leakage, will embrace Smart City mobile e-Health applications and reap their benefits. Hence, in this paper we propose an OAuth2-based protocol with strong user privacy preservation that addresses these privacy issues. Our proposed protocol follows the OAuth2 protocol flow and integrates a pseudonym-based signature scheme and a delegation signature scheme into the user authentication phase of the OAuth2 protocol. The proposed protocol enables citizens authentication towards the servers providing personalized e-Health services, while preserving their privacy from malicious mobile applications and/or eavesdroppers. Moreover, the proposed protocol does not require to store sensitive information in the citizens' mobile devices.

Due to the upsurge of context-aware and proximity aware applications, device-to-device (D2D) enabled mobile cloud (MC) emerges as next step towards future 5G system. There are many applications for such MC based architecture but mobile data offloading is one of the most prominent one especially for ultra dense wireless networks. The proposed system exploits the short range links to establish a cluster based network between the nearby devices, adapts according to environment and uses various cooperation strategies to obtain efficient utilization of resources. We proposed a novel architecture of MC in which the total coverage area of a eNB is divided into several logical regions (clusters). Furthermore, UEs in the cluster are classified into Primary Cluster Head (PCH), Secondary Cluster Head (SCH) and Standard UEs (UEs). Each cluster is managed by selected PCH and SCH. An algorithm is proposed for the selection of PCH and SCH which is based on signal-to-interference-plus-noise (SINR) and residual energy of UEs. Finally each PCH and SCH distributes data in their respective regions by efficiently utilizing D2D links. Simulation results demonstrate that the proposed D2D-enabled MC based approach yields significantly better gains in terms of data rate and energy efficiency as compared to the classical cellular approach.

Coping with the extreme growth of the number of users is one of the main challenges for the future IEEE 802.11 networks. The high interference level, along with the conventional standardized carrier sensing approaches, will degrade the network performance. To tackle these challenges, the Dynamic Sensitivity Control (DSC) and the BSS Color scheme are considered in IEEE 802.11ax and IEEE 802.11ah, respectively. The main purpose of these schemes is to enhance the network throughput and improve the spectrum efficiency in dense networks. In this paper, we evaluate the DSC and the BSS Color scheme along with the PARTIAL-AID (PAID) feature introduced in IEEE 802.11ac, in terms of throughput and fairness. We also, exploit the performance when the aforementioned techniques are combined. The simulations show a significant gain in total throughput when these techniques are applied.

This paper focuses on one of the key enabling technology that will compose future 5G network, the Direct‐LTE communication underlying a cellular infrastructure, also commonly known as Device‐to‐Device (D2D). Energy efficiency algorithms are proposed for the communication between D2D users and cellular users (CUs) and, following the Lagrangian duality theory, an optimal power and rate control solution is given for D2D users, while satisfying the interference limits related to CUs. Finally, the new algorithm is used to achieve proportional fairness between D2D users and CUs and to show with numerical results that the interference to CUs can be limited to always be under a predefined threshold.

To further enhance the energy efficiency (EE) performance of fifth generation (5G) Internet of Things systems, an integrated structure is proposed in this paper. That is, other than prior studies that separately study the wireless and wired parts, the wireless and wired parts are holistically combined together to comprehensively optimize the EE of the whole system. The integrated system structure is introduced beforehand with the proposed unified control center components for better deployment of the select-and-sleep mechanism. In addition, in the wireless part, one cellular partition zooming (CPZ) mechanism is proposed. In contrast, in the wired part, a precaching mechanism is introduced. With these proposals, the proposed system EE performance is investigated. Comprehensive computer-based simulation results demonstrate that the proposed schemes display better EE performance. This is due to the fact that system power consumption is further reduced with these schemes as compared to the prior work.

In this paper, the energy-efficient resource scheduling is investigated considering quality of service (QoS) constraints for OFDMA based heterogeneous network (HetNet). The energy efficiency (EE) is measured as “throughput (bits) per Joule”, while the power consumption model includes RF transmit and circuit power (i.e. feeder loss, cite cooling, signal processing). Compared to existing literature on QoS aware method which mainly consider sum throughput or total transmission power as objective function for optimization, we investigate and model an optimization problem to maximize the EE subject to users' QoS requirements. Given QoS (data rate) requirement for maximizing EE, a constrained based optimization problem is devised. Since the optimization problem is non-convex in nature, we reconstruct the optimization problem as a convex one and devise a pragmatically efficient novel resource assignment algorithm for maximizing achievable EE with quick convergence. The considered optimization problem is transformed into a convex optimization problem by redefining the constraint using the cubic inequality satisfying certain conditions to prove non-negativity, which leads to the problem to be solve as a convex optimization method and results in an efficient iterative resource allocation algorithm. In each iteration, the transformed problem is solved by using Lagrangian dual decomposition with a projected gradient method. Analytical insights and numerical results exhibit the potency of the devised scheme for the targeted complex wireless systems. Our analysis also provides design guidelines for the future design and deployment of green wireless networks as approach the 5G era.

Security is of paramount importance for vehicular communications to verify regular updates from authorized vehicles while detecting misleading reports from malicious or malfunctioning on board units (OBUs). This should however be provided without violating the privacy of the users. Using frequently changing pseudonyms for the vehicles is an effective solution to preserve their privacy. However, in current systems the vehicles need to be always connected to the Certification Authority (CA) to receive the pseudonyms. This might be unfeasible due to the sparse infrastructure deployment, especially in the early stage of the technology, that can lead to availability and scalability problems. In this paper, we solve this issue by proposing efficient protocols for authentication, pseudonym generation, message verification and anonymity revocation that do not require permanent contact with the CA. Even in case of network availability, this feature also reduces the communication overhead for the pseudonym generation and revocation list distribution.

This paper addresses a game-theoretic based distributed appliance scheduling approach for demand-side management in smart grid. The game is played by a utility company and its subscribed residential users. The company monitors electricity demand online, manipulates the price of electricity for different hours to shape users' consumption pattern, and advertises it through Smart Grid infrastructure. Monitoring the electricity price in the market, users independently react by optimally scheduling their shiftable appliances to minimise their electricity expenses. We study day-ahead and quadratic pricing tariffs and discuss the best strategic response of users to each of these strategies that achieves Nash Equilibrium. The game serves as a distributed optimisation tool to minimise load variation in the grid. That is, even if users selfishly minimise their electricity expenses, they will automatically contribute to minimise peak-to-average ratio (PAR) of the aggregate demand, too. Simulation results confirm that adopting quadratic pricing tariff by company and quadratic programming scheduling by users is a Pareto-optimal strategy profile that can achieve a drastic PAR shaving while cutting users electricity expenses by half.

Information paths in current communication networks can often be modelled by set of serial links interconnected by intermediate nodes. These kind of scenarios are called line networks. If the links between nodes experience connection problems, burst erasures of information can happen. In this article, hybrid serial concatenated network codes are proposed, which consist in the serial concatenation of a 'classical' coding scheme and a network code. A novel way to decrease complexity of this approach is that the 'classical' coding scheme is not performed on a link level. The new coding schemes improve performance of the communications in line networks in terms of error- correcting capability. The evaluations involve MATLAB simulations and analytical analysis.

Traditional packet scheduling is mainly designed for increasing spectral efficiency (SE) but not for the energy efficiency (EE). Self-organized network (SON) has prospective for self- configuring, self-optimizing self-healing and minimizes the energy consumption in the network. We consider self-optimizing and self-healing property of SON and investigate a novel energy efficient scheduling algorithm for LTEA. We first compare the state the of art scheduling in view of energy efficiency, then explain the tradeoff between EE and SE. System level simulation (SLS) analysis shows that the investigated SON approach achieves notable energy gain over traditional scheduling algorithm.

Abstract Cognitive radio is envisaged as a promising solution to cope with the problem of spectrum scarcity. In cognitive radio networks, users can sense the medium and opportunistically use available frequency bands. Users can cooperate in order to increase the reliability of the sensing process, which is called cooperative spectrum sensing (CSS). However, cooperative paradigms are threatened by the behavior of malicious users. Two types of attacks represent the main threats for CSS network operation, namely, spectrum sensing data falsification (SSDF) and incumbent emulation (IE). These two types of attacks have received a considerable amount of attention in the literature, but they have always been studied separately. In this paper, we propose a novel mechanism based on lightweight cryptography that considers both SSDF and IE attacks combined in a CSS network for the first time. Lightweight cryptography, in contrast to previous techniques used (such as intrusion detection or reputation systems), provides higher resilience to such attacks when a high number of mobile malicious users exist, providing better energy efficiency. Analytical and simulation results show the outperformance of the proposed algorithm compared with previous mechanisms, in terms of lower false alarm probability (i.e., higher chance of using the free frequency bands) and hence better energy ratings. Copyright © 2015 John Wiley & Sons, Ltd.

The increasing energy consumption driven by striking growths in the number of users and data usage turns out the focal point for mobile operators in fulfilling requirements on cost reduction and environmental impact targets. As a step towards incorporating more energy friendly mobile platforms in future networks, 3GPP LTE-Advanced has adopted coordinated multipoint (CoMP) transmission/reception due to its ability to mitigate and/or coordinate inter-cell interference (ICI). The major CoMP techniques which already existed are joint transmission (JT) and coordinated scheduling/ beamforming (CS/CB). In this paper we propose a novel energy-efficient design (NEED) for heterogeneous network (HetNet) CoMP architecture composing both JT and CS/CB which provides a realistic trade-off for green wireless networks. A feedback based ICI coordination scheme is investigated in terms of different performance metrics (throughput; cell average and cell-edge energy efficiency; radio signaling and backhaul overhead comparison; and power consumption ratio) which eventually helps to reach a decision in favor of the proposed architecture in terms of performance trade-off.

Interference management is an important subject in Device to Device (D2D) communication when underlying a LTE-A cellular band. By default, in LTE-A there is negligible intra-cell interference due to the orthogonality of the subcarriers but this orthogonality will be lost when D2D communication takes place under cellular users (CU). Therefore, one of the key aspects of D2D communication is the set of spectrum bands in which D2D communication takes place. Hence, in this paper we will propose two novel resource allocation (RA) schemes: the first RA scheme (cell level) mitigates the interference between D2D and CU and the second RA scheme (user level or scheduling) schedules the resources in an energy efficient way between D2D and CU. These RA schemes increase the throughput and reduce the overall energy cost per bit of the system. Afterwards, these schemes are compared with the conventional methods and the simulation results show that the proposed schemes obtain higher throughput and save significant amount of energy per bit.

Multi-standard mobile terminals (MTs) allow mobile users to experience ubiquitous connectivity and better quality of service. However, these advances come at a price of higher power consumption for MTs due to holding multiple active interfaces. In this paper, we apply multihop relaying through pervasive short range interfaces (e.g., Bluetooth, WiMedia. etc.) in the presence of an infrastructure network (e.g., LTE, WiFi, etc.) to extend the battery lifetime of multi-standard MTs. To this end, MTs exchange context information (conveying channel state information, battery level, etc.) and, whenever beneficial, form a coalition, pool their resources, and perform their tasks cooperatively to enhance their efficiency. We introduce a novel utility function to assess the profitability of cooperation. To incentivize MTs to cooperate, the achieved common utility of the coalition can be distributed among the cooperative MTs by means of energy credits using a solution concept from coalitional game theory. The simulation results validate the effectiveness of the proposed approach to extend the battery lifetime of MTs to more than double.

Hierarchical ad hoc networks have gained popularity due to the huge number of mobile devices, as well as the social content distribution which is frequently location based. One major issue of hierarchical networks due to mobility is the overlapping of clusters. The issue is often resolved by merging of overlapping clusters. Some efforts propose immediate merging of clusters, while others suggest contention intervals before merging without detailed analysis of the length of such intervals. The literature lacks a thorough study of the efficiency of merging procedures. This paper represents a comprehensive study of the merging procedures. The paper derives an analytical model to compare the merging procedure with and without applying contention intervals. The effect of varying the length of contention intervals on the cluster stability and energy efficiency is also studied. The results show that adopting contention intervals is not recommended. Instead, to achieve the best energy efficiency when two clusters overlap, the clusters should either merge immediately or avoid merging, depending on the characteristics of the scenario. The analytical results are supported with simulations.

Cooperative communications have been gaining huge attention lately, due to the increase in the number of mobile devices and the advancement in their capabilities. In this framework, a commonly suggested approach to benefit from cooperation is the formation of virtual groups of mobile terminals. Mobility-aware algorithms are commonly proposed, where nodes form cooperative clusters according their basic mobile characteristics. However, more detailed mobility pattern information is not used to improve the performance of these systems. The reason is the complexity of current mobility pattern recognition algorithms that require previous set up and advanced knowledge on the scenario. This paper presents a novel cognitive algorithm for the identification of mobility patterns that can be used for mobile group formation and that requires no configuration. The proposed algorithm, based on data mining techniques and cooperative optimization, can evaluate the number of mobility patterns in the scenario and classify nodes according these patterns in a distributive fashion. The algorithm is mathematically presented and tested with extended simulations in both Matlab and ns-2.

Coordinated Multipoint systems (CoMP) have spurred considerable research activity in recent years as they enable both capacity and coverage improvement over conventional cellular systems with open issues in the design of radio resource management algorithms for this type of systems with the challenge of multiple sub-carriers resources available in the OFDMA (orthogonal frequency division multiple access) based systems. To partially solve these issues this paper proposes a user scheduling and resource allocation algorithm for distributed broadband wireless systems based on OFDMA (orthogonal frequency division multiple access) for the 3D video streaming service. The study considers a Manhattan network which is one of the most promising scenarios for CoMP. The algorithm combines the concepts of maximum carrier-to-interference scheduling and antenna selection to increase throughput and ensure zero intra-cell interference. The results show that the use of CoMP can provide significant advantages over conventional deployments in the 3D video streaming services.

The diffusion of mobile devices, that are capable to play videos, opened new challenges for video streaming applications. Since wireless communications are prone to erasures, an important goal is to research possible ways to reliably provide video content. Fountain codes are an efficient way to protect video streaming against erasures: in particular, burst erasures are the ones that can significantly reduce the quality of the transmitted video. In this work, Luby codes for burst erasure channels are investigated. Our main focus is on the design of Luby codes to make the receiver able to recover all the lost information without any retransmission. Furthermore, we analytically demonstrate that lower energy consumption is achievable by using LT codes instead of a general retransmission scheme.

An inherent weakness of network coding is that it is vulnerable to pollute attacks from adversaries, which may jeopardize its advantages such as throughput enhancement. Message Authentication Code (MAC) is commonly used scheme to provide integrity against data pollution by appending data and shared-key dependent tags. However, this scheme results in a new type of pollution called tag pollution, where a packet with polluted tags may traffic multiple hops before detection and result in network bandwidth waste. We propose a hybrid technical scheme, called MSXOR, which can detect tag pollution with low communication and computation overhead. We address the pollution attacks by designing an efficient MAC scheme suitable for multicast XOR network coding, and tag pollution attacks by appending a signature of all the MACs. Our experimental results show that it slightly increases the computation complexity, for the sake of resisting to tag pollution.

Multiuser MIMO (MU-MIMO), in which the base station transmits multiple streams to multiple users, has received significant importance as a way of achieving spectral efficiency (SE) and energy efficiency (EE). However, these two important design criteria conflict with each other and a careful study of their trade-off is mandatory for designing future wireless communication systems. This paper investigates the trade-off between EE and SE in downlink MU-MIMO system which is adopted by 3GPP LTE-Advanced to meet IMT-Advanced targets. The EE is measured as, “throughput (bits) per Joule” while both RF transmit power and circuit power consumptions are considered. In this paper, given the SE requirement, a constrained optimization problem where constraints redefined with cubic inequality is formulated to maximize EE. Then, a novel resource allocation algorithm is proposed to achieve maximum EE. Simulation results demonstrate the effectiveness of the proposed scheme and illustrate the fundamental trade-offs between energy efficient and spectral efficient transmission. Our analytical results shed light on future “green” network planning in downlink MU-MIMO systems.

Packet forwarding is an essential service in a multihop cellular network (MCN), which relies on the cooperation of all participating mobile terminals (MTs). Although this service can effectively improve the energy efficiency of MTs by dividing a long radio link to several shorter hops, cooperation of MTs cannot be taken for granted as they are normally controlled by rational players who seek to maximize their own benefit by taking advantage of the service while minimizing their contribution. To ensure proper operation of the network, there should be a mechanism to encourage cooperative nodes while punishing free riders. To this end, we propose a credit scheme based on coalitional game model. We provide credit to cooperative nodes proportional to the core solution of the game, which distributes the common utility among the players in a way that everyone is satisfied. Simulation results validate that the proposed technique can successfully detect and isolate selfish nodes.

This paper studies the performance of network coding (NC) based system, especially looking at the implications in surveillance applications. We argue that NC can provide advantages from a data throughput perspective; however, an analysis of NC mechanism, especially when there are erroneous channels in the network, is required. We study the performance of the NC mechanism based on several parameters for different scenarios categorized by the error-free and erroneous communication channels that introduce random errors either at packet or at symbol level.

In an era where spectral resources are at a premium, the underutilization of LTEA spectrum on the Uplink (UL) band due to the asymmetric nature of internet traffic is a major concern. On the other hand, Ad-hoc mode, Device to Device (D2D) or Direct-LTEA communication underlying a cellular infrastructure has recently started gaining attention as a new technology that supports machine-to-machine scenarios that can be network unassisted providing a tentative solution towards filling in the spectral gaps. Although D2D communication can share the same licensed frequency band with cellular users, it requires stringent interference management to avoid diminishing the communication quality with primary users. In this paper, we consider secondary D2D users that operate over LTEA UL bands. D2D users sense the pathloss between their location and LTEA eNB. Based on this sensing information, a novel resource allocation scheme is applied to mitigate the interference with the LTEA system. Simulation results show that there is about a 50

MoSHCA is a mHealth project designed to improve patient-doctor interaction and to promote the self-management of chronic diseases by the patients themselves. The number of people with a chronic disease is dramatically increasing worldwide. This is becoming a major obstacle for economic stability and growth and the sustainability of national health care systems. The introduction of self-management by patients with a chronic disease seems inevitable as a countermeasure against these developments. MoSHCA provides intelligent, user-friendly and secure, medical and well-being decision support through embedded software in mobile devices by utilizing specific sensors and data from customized information systems.

Human movement is strongly affected by the needs of humans to socialize or cooperate with each other, in one form or another, which results in some degree of clustering and correlation with their movements. In this paper, we exploit the movement correlations, and propose a new community-based sequential paging algorithm to reduce the paging signaling overhead. Our simulation results demonstrate that given the same location update scheme employed in the LTE-A network, the proposed algorithm is capable of reducing 20% of the paging messages in the best scenario when having high moving rate.

In Mobile Ad hoc NETworks (MANETs), where cooperative behaviour is mandatory, there is a high probability for some nodes to become overloaded with packet forwarding operations, in order to support neighbour data exchange. This altruistic behaviour leads to an unbalanced load in terms of traffic and energy consumption. In such scenarios, mobile nodes can benefit from the use of an energy efficient and traffic fitting routing protocol, that better complies with their limited battery capacity and throughput. This paper presents E2DYMO, an improvement to the well-known DYMO protocol that offers energy efficiency and fairness in routing for mobile wireless ad hoc networks. With this proposed improvement, nodes are able to find several routes per each pair of source and destination nodes and select the best route according to energy and traffic parameters, effectively extending the lifespan of the network.

Energy is becoming a vital resource in wireless networks and with the introduction of high data rate wireless technologies the future Wireless Personal Area Networks (WPANs) will be able to support demanding applications e.g. interactive games, video sharing etc. and as a result the energy saving mechanisms will become more demanding and challenging. The mobile devices with hybrid character, containing ad hoc and infrastructure access, will need mechanisms that keep them connected in an energy efficient way. For the node to be connected to either a cluster or a solitary node knowledge of the neighbor devices are required to choose a suitable node for cooperative communication; this discovery of information is under the scope of this paper. We present a context based beaconing mechanism both for node discovery and cooperative cluster formation. We have exploited the available beacon Information Elements (IEs) of UWB to embed our own information elements of interest e.g. Energy level, status of node, willingness, etc. to facilitate node discovery and cooperation. A Context Extraction Function (CxEF) has been created to insert and extract information in the beacon. We have also presented a detailed representation of bit mapping for energy and cooperation in beacons. The simulation results show that our mechanism saves a considerable amount of energy compared to other techniques which perform blind node discovery without using context information.

It is necessary to re-think the current access selection polices to the simultaneously available wireless networks, whose number is growing, and the appropriate management mechanisms, to enhance the experience for the final user, for example in terms of QoS and energy efficiency. The IEEE 802.21 standard introduces link layer intelligence as well as related network information to upper layers in order to optimize handovers between networks of different technologies (WiMAX, Wi-Fi, 3GPP, etc.). As the integration of multiple wireless interfaces in mobile terminals becomes ordinary it is important to efficiently manage those interfaces. Not only to conveniently provide the requested services to the user, but also to do that in an energy efficient way in order to allow higher mobility by extending the battery life of its mobile terminal. In this paper the IEEE 802.21 standard is used while considering handovers between WiMAX and Wi-Fi, through an implementation in ns-2 introducing a simple, but effective, location-assisted energy saving technique.

This work focus on a coexistence study between wireless microphone systems and secondary users of the TV White Spaces, using a Monte-Carlo methodology. Exclusion areas around wireless microphone receivers, for co-channel and adjacent channel interference, are computed, considering indoor and outdoor scenarios. Using this methodology, impact and tendencies of several parameters over the probability of interference are analyzed, like spectral channel spacing, separation distance and propagation scenario. As an example, for outdoor scenarios, the spectral spacing between primary system and secondary users, ranging from 0 MHz (co-channel operation) to 16 MHz (2 DVB-T channels) results in a protection distance of 13.9 km and 2.2 km, respectively.

Coordinated MultiPoint (CoMP) transmission / reception techniques have been adopted by the 3GPP community for LTE-Advanced (Long Term Evolution-Advanced) to increase spectral efficiency and cell-edge throughput. Energy efficiency (EE) was previously ignored by most research efforts and was not considered by 3GPP as an important performance indicator until very recently. EE concerns have spurred considerable research in recent years as they enable both energy consumption and CO2 reduction over conventional cellular systems. As the “green evolution” becomes a major trend, energy-efficient transmission becomes more and more important. In this paper we investigate energy efficiency i.e., the number of corrected bits that can be transmitted throughout the channel per joule of energy consumed of different downlink CoMP techniques in LTE-Advanced. In addition, we use system level simulation to evaluate the performance.

Recent years have witnessed an explosive growth of wireless technologies and applications. The adoption of a wide range of multimedia applications on wireless devices has been made possible through the advances in wireless technologies. Only one aspect is not catching up, which is re-chargeable battery for mobile devices. Without disruptive new approaches for energy saving, mobile users will relentlessly be searching for power outlets rather than network access, and become once again bound to a single location. In this paper, we address the cooperation among wireless nodes in a cluster for the main goal of saving the battery power of mobile terminals. This paper studies the possibility of decreasing energy consumption through relaying data over short range technologies through cluster head. Relaying nodes sacrifice resources for the sake of other devices; hence a trading model based on Cournot model is proposed to offer incentives for relays. Simulation results demonstrate the gains in energy saving. The proposed algorithm also guarantees that the required QoS is always maintained.

The growing presence of concurrent heterogeneous wireless access networks, together with the increasing service demands from the end-users, require re-thinking of current access selection polices and appropriate management mechanisms, namely concerning quality of service, energy efficiency, etc. The recent IEEE 802.21 standard introduces link layer intelligence as well as related network information to upper layers in order to optimise handovers between networks of different technologies, such as WiMAX, Wi-Fi and 3GPP. With the massification of mobile terminals with multiple wireless interfaces it is important to efficiently manage those interfaces not only to appropriately provide the requested services to the user, but also to do that in an energy efficient way in order to allow higher mobility to the user by extending the battery life of its terminal. The study the IEEE 802.21 standard is briefly introduced, presented in the signalling in a handover between WiMAX and Wi-Fi, and exploited through an implementation in ns-2 introducing a simple, but effective, energy-saving approach.

MANETs may be deployed in places where communication is required but no infrastructure is present, such as in many infrastructure-less scenarios where a SIP central server model becomes a central point of failure. This necessitates the design of an overlay architecture such as Peer to Peer SIP above the transport layer to provide services for delay sensitive applications such as VoIP and Video. Currently there is no proper correlation between MANETs and P2PSIP. In this paper we propose a novel cross-layered solution design for handling the P2PSIP overlay. This solution provides useful information that can address the key research challenges surrounding availability and reliability of P2PSIP within mobile ad hoc networks.

Cooperative positioning is a popular approach for indoor localization, which significantly outperforms conventional positioning techniques. This paper presents a method for iterative node localization, with a relatively small number of initial anchor nodes. In order to reduce error propagation in iterative schemes, it is necessary to use only reliable information among nodes. We introduce a reference node selection strategy based on utility functions. The method is completely distributed and involves only information from local neighborhood. Compared to the nearest neighbor and random node selection scheme, simulation results show that selecting subsets of nodes with highest utility values leads to more accurate position estimates.

In this paper, we describe GREENET (an early stage training network in enabling technologies for green radio), which is a new project recently funded by the European Commission under the auspices of the 2010 Marie Curie People Programme. Through the recruitment and personalized training of 17 Early Stage Researchers (ESRs), in GREENET we are committed to the development of new disruptive technologies to address all aspects of energy efficiency in wireless networks, from the user devices to the core network infrastructure, along with the ways the devices and equipment interact with one another. Novel techniques at the physical, link, and network layers to reduce the energy consumption and carbon footprint of 4G devices will be investigated, such as Spatial Modulation (SM) for Multiple-Input-Multiple-Output (MIMO) systems, Cooperative Automatic Repeat reQuest (C-ARQ) protocols, and Network Coding (NC) for lossy networks. Furthermore, cooperation and cognition paradigms will be exploited as additional assets to improve the energy efficiency of wireless networks with the challenging but indispensable constraint of optimizing the system capacity without degrading the user's Quality-of-Service (QoS).

Multi-standard mobile devices are allowing users to experience higher data rates and ubiquitous connectivity. These advances are achieved on the expense of higher energy consumption due to the multiple active wireless interfaces. In this paper, we use one advantage of the multiple interfaces, namely short range communications. Mobile terminals (MTs) form short range cooperative network to take advantage of the good channel quality of short range links to save energy of MTs. In this cooperative network, the energy of all MTs is treated as a pool of resources, which is available for all MTs in the network. Toward this end, we propose a combinatorial optimization game to identify the problem. We derive the core solution for the relay selection game in cooperative networks. A mathematical framework to compute the achieved energy saving is derived. Simulation results using the solution of the game show that energy saving can be achieved using short range cooperation in wireless networks. The results also show the effectiveness of the proposed node selection game.

In ad hoc networks some nodes can became a critical spot in the network because they support packet forwarding for most of their neighbours. Critical nodes would consume more energy due to the extra load and deplete battery sooner. These unfairly burdened nodes will lead to node failure, network partitioning, decrease in route lifetime and route reliability. To avoid this problem, this paper proposes a new routing protocol, called Energy Efficient DSR (E2DSR) for balancing the energy consumption amongst the nodes in the network. E2DSR uses some mechanisms of Dynamic Source Routing (DSR) but defines a new structure for control packets, changes the routing behavior in nodes, implements a new "Energy Table" and creates a whole new algorithm for route cache and selection.

This paper describes a new security framework that will leverage the security in cognitive radio cooperative protocols. The architecture uses the 802.1X access control mechanism and is supported by a Key Distribution Center (KDC) built upon an 802.1X Authentication Server. The KDC is used, together with a new terminal identification policy and modified DHCP servers, to provide proper resource allocation and message authentication in DHCP transactions. The KDC is also used to authenticate mappings between layer 2 and 3 addresses (ARP protocol) and to distribute session keys to neighbor cognitive radio terminals, allowing them to set up other peer-to-peer secure interactions using such session keys.

In beyond 3G systems, one of the important factors is to address radio access technology (RAT) selection and load balancing between heterogeneous networks to ensure high spectral efficiency in an era where spectral resources are at a premium. This work aims to address the feasibility of utilizing WiFi as complementary service for HSDPA, to prevent quality of service deterioration in the event of network overload in HSDPA during the busy period. The proposed RAT selection algorithm is based on the load of each system, and the results show that the outage probability can be improved by up to 45% relative to a stand-alone HSDPA system.