info@itechprosolutions.in | +91 9790176891

NS2 2016 Projects

Category Archives

Seeing Is Believing: Sharing Real-time Visual Traffic Information via Vehicular Clouds

Abstract

         From today’s conventional cars to tomorrow’s self-driving cars, advances in technology will enable vehicles to be equipped with more and more-sophisticated sensing devices, such as cameras. As vehicles gain the ability to act as mobile sensors that carry useful traffic information, people and vehicles are sharing sensing data to enhance the driving experience. This paper describes a vehicular cloud service for route planning, where users collaborate to share traffic images by using their vehicles’ on-board cameras. We present the architecture of a collaborative traffic image–sharing system called Social Vehicle Navigation (SVN), which allows drivers in the vehicular cloud to report and share visual traffic information called Navi Tweets. A set of Navi Tweets is then filtered, refined, and condensed into a concise, user-friendly snapshot summary of the route of interest, called a Traffic Digest. These digests can provide more pertinent and reliable information about the road situation and can complement predictions like estimated time of arrival, thereby supporting users’ route decision making. As proof of concept, this paper presents the system design and a prototype implementation running on the Android smartphone platform, along with its evaluation.


Load Aware Self-Organising User-Centric Dynamic CoMP Clustering for 5G Networks

Abstract

     Coordinated multi-point (CoMP) is a key feature for mitigating inter-cell interference, improve system throughput, and cell edge performance. However, CoMP implementation requires complex beamforming/scheduling design, increased backhaul bandwidth, additional pilot overhead, and precise synchronization. Cooperation needs to be limited to a few cells only due to this imposed overhead and complexity. Hence, small CoMP clusters will need to be formed in the network. In this paper, we first present a self-organizing, user-centric CoMP clustering algorithm in a control/data plane separation architecture, proposed for 5G to maximize spectral efficiency (SE) for a given maximum cluster size. We further utilize this clustering algorithm and introduce a novel two-stage re-clustering algorithm to reduce high load on cells in hotspot areas and improve user satisfaction. Stage-1 of the algorithm utilizes maximum cluster size metric to introduce additional capacity in the system. A novel re-clustering algorithm is introduced in stage-2 to distribute load from highly loaded cells to neighboring cells with less load for multi-user joint transmission CoMP case. We show that unsatisfied users due to high load can be significantly reduced with minimal impact on SE.


Secure Transmission of Optical DFT-S-OFDM Data Encrypted by Digital Chaos

 Abstract

     This paper proposes and demonstrates a physical layer security-enhanced transmission scheme for discrete Fourier transform spread orthogonal frequency-division multiplexing (DFT-S-OFDM) signals in a passive optical network. During the DFT-S-OFDM transmission, the reconfigurable DFT matrix, the subcarrier allocation, and the training sequence for OFDM symbol synchronization are well encrypted by digital chaotic sequences generated via a 4-D hyperdigital chaos, which provides a huge key space of ~1045. Secure transmission of 13.3-Gb/s encrypted DFT-S-OFDM signals is demonstrated over a 20-km standard single-mode fiber, which proves the excellent confidentiality of the proposed secure DFT-S-OFDM transmission. Moreover, the receiver sensitivity is improved ~2 dB (BER@10-3), which is due to the effective reduction of peak-to-average power ratio via DFT precoding. In addition, this scheme has the advantages of low computational complexity and has no requirement of redundant sideband information.


Performance of Quantized Random Beamforming in Delay-Tolerant Machine-Type Communication

Abstract

        Machine-to-Machine (M2M) communication represents a new paradigm for mobile cellular networks, where a massive number of low-cost devices requests the transfer of small amounts of data without human intervention. One option to tackle this problem is obtained by combining Random Beamforming (RBF) with opportunistic scheduling. RBF can be used to induce larger channel fluctuations and opportunistic scheduling can be used to select M2M devices when their overall channel quality is good. Traditional RBF does not fulfill M2M requirements because overall channel quality needs to be tracked continuously. In order tackle this limitation, a novel codebookbased RBF architecture that identifies in advance the time instants in which overall channel quality should be reported, within a coherence time window, is proposed. This opportunistic feedback mechanism reduces signaling overhead and enables energy saving at M2M devices. A simplified methodology is presented to evaluate the system mean data rate, using for this purpose closed form formulas derived from SNR distribution approximations. Results reveal that the performance loss that is experienced for introducing the proposed modifications to traditional RBF scheme is negligible. The concepts analyzed in this paper provide useful insights, and show that codebook-based RBF with simplified opportunistic scheduling algorithms is an excellent combination to provide wide-area M2M services with low-cost devices and limited signaling overhead.


Exploiting Trust and Usage Context for Cross-Domain Recommendation

Abstract

       Cross-domain recommender systems are usually able to suggest items, which are not in the same domain, where users provided ratings. For this reason, cross-domain recommendation has attracted more and more attention in recent years. However, most studies propose to make cross-domain recommendation in the scenario, where there are common ratings between different domains. The scenario without common ratings is seldom considered. In this paper, we propose a novel method to solve the cross-domain recommendation problem in such a scenario. We first apply trust relations to the cross-domain scenario for predicting coarse ratings pertaining to cross-domain items. Then, we build a new rating matrix, including known ratings and predicted ratings of items from different domains, and transform a user-item matrix into an item-item association matrix. Finally, we compute the similarities of items belonging to different domains and use item-based collaborative filtering to generate recommendations. Through relevant experiments on a real-world data set, we compare our method to a trust-aware recommendation method and demonstrate its effectiveness in terms of prediction accuracy, recall, and coverage.


A Distributed SON-based User-Centric Backhaul Provisioning Scheme

Abstract

       5G definition and standardization projects are well underway, and governing characteristics and major challenges have been identified. A critical network element impacting the potential performance of 5G networks is the backhaul, which is expected to expand in length and breadth to cater to the exponential growth of small cells while offering high throughput in the order of Gbps and less than one-millisecond latency with high resilience and energy efficiency. Such performance may only be possible with direct optical fibre connections which are often not available countrywide and are cumbersome and expensive to deploy. On the other hand, a prime 5G characteristic is diversity, which describes the radio access network, the backhaul, and also the types of user applications and devices. Thus, we propose a novel, distributed, selfoptimized, end-to-end user-cell-backhaul association scheme that intelligently associates users with potential cells based on corresponding dynamic radio and backhaul conditions while abiding by users’ requirements. Radio cells broadcast multiple bias factors, each reflecting a dynamic performance indicator (DPI) of the endto- end network performance such as capacity, latency, resilience, energy consumption, etc. A given user would employ these factors to derive a user-centric cell ranking that motivates it to select the cell with radio and backhaul performance that conforms to the user requirements. Reinforcement learning is used at the radio cell to optimize the bias factors for each DPI in a way that maximizes the system throughput while minimizing the gap between the users’ achievable and required end-to-end quality of experience (QoE). Preliminary results show considerable improvement in users QoE and cumulative system throughput when compared to state-of-theart user-cell association schemes.


Energy Efficient User Association for Cloud Radio Access Networks

Abstract

         Cloud radio access network (C-RAN) and massive multiple-input multiple-output (MIMO) are recognized as two key technologies for the fifth-generation (5G) mobile networks. In this paper, we consider the energy efficiency-based user association problem in massive MIMO empowered C-RAN, where multiple antennas are clustered at each remote radio head (RRH). We first obtain the deterministic equivalent expression of the energy efficiency, and then propose three user association algorithms, named nearest-based user association (NBUA), single- candidate RRH user association (SCRUA), and multi-candidate RRHs user association (MCRUA), respectively. In NBUA and SCRUA, each user is associated with only one RRH, and in MCRUA, multiple RRHs can serve the same user. In our algorithms, the impact of power consumption of fronthaul links and antennas are considered by allowing inefficient RRHs to be turned into sleep mode. We provide numerical comparisons of the proposed algorithms and a state-of-the-art baseline which associates each user with the nearest RRH. The results show that our proposed algorithms achieve higher energy efficiency than the baseline algorithm. The proposed MCRUA algorithm achieves a good balance between spectral and energy efficiency, and the performance gain is more significant when the number of users is large.


PRISM: PRivacy-aware Interest Sharing and Matching in Mobile Social Networks

Abstract

        In a profile matchmaking application of mobile social networks, users need to reveal their interests to each other in order to find the common interests. A malicious user may harm a user by knowing his personal information. Therefore, mutual interests need to be found in a privacy preserving manner. In this paper, we propose an efficient privacy protection and interests sharing protocol referred to as PRivacy-aware Interest Sharing and Matching (PRISM). PRISM enables users to discover mutual interests without revealing their interests. Unlike existing approaches, PRISM does not require revealing the interests to a trusted server. Moreover, the protocol considers attacking scenarios that have not been addressed previously and provides an efficient solution. The inherent mechanism reveals any cheating attempt by a malicious user. PRISM also proposes the procedure to eliminate Sybil attacks. We analyze the security of PRISM against both passive and active attacks. Through implementation, we also present a detailed analysis of the performance of PRISM and compare it with existing approaches. The results show the effectiveness of PRISM without any significant performance degradation.


Enhanced Deployment Algorithms for Heterogeneous Directional Mobile Sensors in a Bounded Monitoring Area

Abstract

   Good deployment of sensors empowers the network with effective monitoring ability. Different from omnidirectional sensors, the coverage region of a directional sensor is determined by not only the sensing radius (distance), but also its sensing orientation and spread angle. Heterogeneous sensing distances and spread angles are likely to exist among directional sensors, to which we refer as heterogeneous directional sensors. In this paper, we target on a bounded monitoring area and deal with heterogeneous directional sensors equipped with locomotion and rotation facilities to enable the sensors self-deployment. Two Enhanced Deployment Algorithms, EDA-I and EDA-II, are proposed to achieve high sensing coverage ratio in the monitored field. EDA-I leverages the concept of virtual forces (for sensors movements) and virtual boundary torques (for sensors rotations), whereas EDA-II combines Voronoi diagram directed movements and boundary torques guided rotations. EDA-I computations can be centralized or distributed that differ in required energy and execution time, whereas EDA-II only allows centralized calculations. Our EDA-II outperforms EDA-I in centralized operations, while EDA-I can be adapted into a distributed deployment algorithm without requiring global information and still achieves comparably good coverage performance to its centralized version. To the best of our knowledge, this is perhaps the first work to employ movements followed by rotations for sensors self-deployment. Performance results demonstrate that our enhanced deployment mechanisms are capable of providing desirable surveillance level, while consuming moderate moving and rotating energy under reasonable execution time.


Coalitional Games for joint Co-tier and Cross-tier Cooperative Spectrum Sharing in Dense Heterogeneous Networks

Abstract

         With the dense deployment of small-cells in the next generation of mobile networks, users from different tiers suffer from high downlink interferences. In this paper, we propose a game theoretic approach for joint co-tier and cross-tier collaboration in heterogeneous networks and analyze the relevance of the proposed scheme. First, we propose a coalition structure game (CS game) with a Weighted Owen value as imputation, where the Small-cell Base Stations (SBSs) and their connecting Macrocell User Equipments (MUEs) form a priori union. We prove that the proposed framework optimizes the users profit. As an additional global benefit, the SBSs are encouraged to host the harmed public users in their vicinity. Secondly, we propose a canonical game with a Weighted Solidarity value as imputation to allow cooperation among SBSs and MUEs when they fail to connect to nearby SBSs. We prove that the weak players are protected in this scheme and that a high degree of fairness is provided in the game. We compare through extensive simulations the proposed frameworks with state-of-the-art resource allocation solutions, access modes and legacy game-theoretic approaches. We show that the proposed framework obtains the best performances for the MUEs and Small-cells User Equipments (SUEs) in terms of throughput and fairness. Throughput gain is in order of 40% even reaching 50% for both types of users.


Software Defined Networking with Pseudonym Systems for Secure Vehicular Clouds

Abstract

         The vehicular cloud is a promising new paradigm, where vehicular networking and mobile cloud computing are elaborately integrated to enhance the quality of vehicular information services. Pseudonym is a resource for vehicles to protect their location privacy, which should be efficiently utilized to secure vehicular clouds. However, only a few existing architectures of pseudonym systems take flexibility and efficiency into consideration, thus leading to potential threats to location privacy. In this paper, we exploit software-defined networking technology to significantly extend the flexibility and programmability for pseudonym management in vehicular clouds. We propose a software-defined pseudonym system, where the distributed pseudonym pools are promptly scheduled and elastically managed in a hierarchical manner. In order to decrease the system overhead due to the cost of inter-pool communications, we leverage the two-sided matching theory to formulate and solve the pseudonym resource scheduling. We conducted extensive simulations based on the real map of San Francisco. Numerical results indicate that the proposed software-defined pseudonym system significantly improves the pseudonym resource utilization, and meanwhile, effectively enhances the vehicles’ location privacy by raising their entropy.


Joint User-Association and Resource-Allocation in Virtualized Wireless Networks

Abstract

       In this paper, we investigate the network utility maximization problem in energy harvesting cognitive radio sensor networks (CRSNs). Different from traditional sensor networks, sensor nodes in CRSNs are embedded cognitive radio modules, enabling them to dynamically access the licensed channels. Since the dynamic channel access is critical to guarantee the network capacity for CRSNs, existing solutions without considering the dynamic channel access cannot be directly applied into CRSNs. To this end, we aim at maximizing the network utility by jointly controlling the sampling rates and channel access of sensor nodes, under the energy consumption, channel capacity and interference constraints. With the consideration of fluctuated energy harvesting rates and channel switching costs, we formulate the network utility maximization as a mix-integer non-linear programming problem and solve it in an efficient and decoupled way by means of dual decomposition. A joint channel access and sampling rate control scheme, named JASC, is then proposed considering the real-time channel sensing results and energy harvesting rates. Extensive simulation results demonstrate that JASC can efficiently improve the network utility in CRSNs based on a realistic energy harvesting dataset.


Energy Efficiency of Multi-user Multi-antenna Random Cellular Networks with Minimum Distance Constraints

Abstract

      Compared with conventional regular hexagonal cellular models, random cellular network models resemble real cellular networks much more closely. However, most studies of random cellular networks are based on the Poisson point process and do not take into account the fact that adjacent base stations (BSs) should be separated with a minimum distance to avoid strong interference among each other. In this paper, based on the hard core point process (HCPP), we propose a multi-user multi-antenna random cellular network model with the aforementioned minimum distance constraint for adjacent BSs. Taking into account the effects of small scale fading and shadowing, interference and capacity models are derived for the multi-user multi-antenna HCPP random cellular networks. Furthermore, a spectrum efficiency model as well as an energy efficiency model is presented, based on which, the maximum achievable energy efficiency of the considered multi-user multiantenna HCPP random cellular networks is investigated. Simulation results demonstrate that the energy efficiency of conventional Poison point process (PPP) cellular networks is underestimated when the minimum distance between adjacent BSs is ignored.


A Multiuser Detection Algorithm in the Uplink SC-FDMA System for Green Communication network

Abstract

         The SC-FDMA communication scheme is the key technique in green communications and networking for 5G for the uplink of SC-FDMA multiusersystemsdue to frequency differences introducingaccess interference. The precision of the traditional parallel and serial interference cancellation algorithms is not high, and the number of required iterations for achieving satisfactory results is large. Anew algorithm for the optimal weighted parallel interference cancellation is proposed to eliminate multiple access interference. This method has higher precision than traditional PIC and the number of interference elimination iterations required is low.


Optimal Demand Response Scheduling with Real Time Thermal Ratings of Overhead Lines for Improved Network Reliability

Abstract

     This paper proposes a probabilistic framework for optimal demand response scheduling in the day-ahead planning of transmission networks. Optimal load reduction plans are determined from network security requirements, physical characteristics of various customer types and by recognising two types of reductions, voluntary and involuntary. Ranking of both load reduction categories is based on their values and expected outage durations, whilst sizing takes into account the inherent probabilistic components. The optimal schedule of load recovery is then found by optimizing the customers’ position in the joint energy and reserve market, whilst considering several operational and demand response constraints. The developed methodology is incorporated in the sequential Monte Carlo simulation procedure and tested on several IEEE networks. Here, the overhead lines are modelled with the aid of either seasonal or real-time thermal ratings. Wind generating units are also connected to the network in order to model wind uncertainty. The results show that the proposed demand response scheduling improves both reliability and economic indices, particularly when emergency energy prices drive the load recovery.


Maximum Lifetime Strategy for Target Monitoring with Controlled Node Mobility in Sensor Networks with Obstacles

Abstract

        Consider a mobile sensor network that is used to monitor a moving target in a field with obstacles. In this paper, an efficient relocation technique that simultaneously maximizes the network lifetime is proposed. The main sources of energy consumption in the network are sensing, communication, and movement of the sensors. To account for this energy consumption, a graph is constructed with edges that are weighted based on the remaining energy of each sensor. This graph is subsequently employed to address the lifetime maximization problem by solving a sequence of shortest path problems. The proposed technique determines a near-optimal relocation strategy for the sensors as well as an energy-efficient route to transfer information from the target to destination. This near-optimal solution is calculated in every time instant using the information obtained through the previous time step. It is shown that by choosing appropriate parameters, sensors’ locations and the communication route from target to destination can be arbitrarily close to their corresponding optimal choices at each time instant. Simulation results confirm the effectiveness of the proposed technique.


Full-Duplex Wireless Communications: Challenges, Solutions, and Future Research Directions

ABSTRACT

            The family of conventional half-duplex (HD) wireless systems relied on transmitting and receiving in different time slots or frequency subbands. Hence, the wireless research community aspires to conceive full-duplex (FD) operation for supporting concurrent transmission and reception in a single time/frequency channel, which would improve the attainable spectral efficiency by a factor of two. The main challenge encountered in implementing an FD wireless device is the large power difference between the self-interference (SI) imposed by the device’s own transmissions and the signal of interest received from a remote source. In this survey, we present a comprehensive list of the potential FD techniques and highlight their pros and cons. We classify the SI cancellation techniques into three categories, namely passive suppression, analog cancellation and digital cancellation, with the advantages and disadvantages of each technique compared. Specifically, we analyze the main impairments (e.g., phase noise, power amplifier nonlinearity, as well as in-phase and quadrature-phase (I/Q) imbalance, etc.) that degrading the SI cancellation. We then discuss the FD-based media access control (MAC)-layer protocol design for the sake of addressing some of the critical issues, such as the problem of hidden terminals, the resultant end-to-end delay and the high packet loss ratio (PLR) due to network congestion. After elaborating on a variety of physical/MAC-layer techniques, we discuss potential solutions conceived for meeting the challenges imposed by the aforementioned techniques. Furthermore, we also discuss a range of critical issues related to the implementation, performance enhancement and optimization of FD systems, including important topics such as hybrid FD/HD scheme, optimal relay selection and optimal power allocation, etc. Finally, a variety of new directions and open problems associated with FD technology are pointed out. Our hope is that this treatise will stimulate future research efforts in the emerging field of FD communications.


Routing Protocol for Heterogeneous Wireless Mesh Networks

Abstract

          The introduction of heterogeneous wireless mesh technologies provides an opportunity for higher network capacity, wider coverage, and higher quality of service (QoS). Each wireless device utilizes different standards, data formats, protocols, and access technologies. However, the diversity and complexity of such technologies create challenges for traditional control and management systems. This paper proposes a heterogeneous metropolitan area network architecture that combines an IEEE 802.11 wireless mesh network with a long-term evolution (LTE) network. In addition, a new heterogeneous routing protocol and a routing algorithm based on reinforcement learning called Cognitive Heterogeneous Routing (CHR) are proposed to select the appropriate transmission technology based on parameters from each network. The proposed heterogeneous network overcomes the problems of sending packets over long paths, island nodes and interference in wireless mesh network and increases the overall capacity of the combined network by utilizing unlicensed frequency bands instead of buying more license frequency bands for LTE. The work is validated through extensive simulations that indicate that the proposed heterogeneous wireless mesh network outperforms the LTE and Wi-Fi networks when used individually. The simulation results show that the proposed network achieves an increase of up to 200% increase in throughput compared with Wi-Fi-only networks or LTE-only networks.


A Gradient-based Coverage Optimization Strategy for Mobile Sensor Networks

Abstract

           A Voronoi-based strategy is proposed to maximize the sensing coverage in a mobile sensor network. Each sensor is moved to a point inside its Voronoi cell  using a coverage improvement scheme. To this end, a gradient-based nonlinear optimization approach is utilized to find a target point for each sensor such that the local coverage increases as much as possible, if the sensor moves to this point. The algorithm is implemented in a distributed fashion using local information exchange among sensors. Analytical results are first developed for the single sensor case, and are subsequently extended to a network of mobile sensors, where it is desired to maximize network-wide coverage with fast convergence. It is shown that under some mild conditions the positions of the sensors converge to a stationary point of the objective function, which is the overall weighted coverage of the sensors. Simulations demonstrate the effectiveness of the proposed strategy.


Apprenticeship Learning based Spectrum Decision in Multi-Channel Wireless Mesh Networks with Multi-Beam Antennas

Abstract

      We propose a novel spectrum decision scheme (i.e., channel selection and handoff) for wireless mesh networks (WMN) which use multiple channels and nodes equipped with multi-beam directional antennas. Our scheme has the following features: (i) It performs spectrum decision by considering various WMN parameters, including the channel quality, beam orientation, antenna-caused deafness and capture effects, and application priority level; (ii) It uses the reinforcement learning (RL)-based spectrum decision process to achieve the optimal quality of multimedia transmission in the long term. However, a newly-joined WMN node could take a long time to make a correct spectrum decision due to the difficult choice of initial RL parameters. Therefore, our scheme uses the apprenticeship learning in conjunction with the RL model, to speed up the spectrum decision process by choosing a suitable neighboring node (called ‘expert’) to teach a newly-joined node (called ‘apprentice’). Our experiments demonstrate that the proposed spectrum decision scheme improves the network performance and multimedia transmission quality.


Efficient Wireless Multimedia Multicast in Multi-rate Multi-channel Mesh Networks

Abstract

Devices in wireless mesh networks can operate on multiple channels and automatically adjust their transmission rates for the occupied channels. This paper shows how to improve performance-guaranteed multicasting transmission coverage for wireless multi-hop mesh networks by exploring the transmission opportunity offered by multiple rates (MR) and multiple channels (MC). Based on the characteristics of transmissions with different rates, we propose and analyze parallel low-rate transmissions (PLT) and alternative rate transmissions (ART) to explore the advantages of MRMC in improving the performance and coverage tradeoff under the constraint of limited channel resources. We then apply these new transmission schemes to improving the WMN multicast experience. Combined with the strategy of reliable interference-controlled connections, a novel MRMC multicast algorithm (LC-MRMC) is designed to make efficient use of channel and rate resources to greatly extend wireless multicast coverage with high throughput and short delay performance. Our NS2 simulation results prove that ART and LC-MRMC achieve improved wireless transmission quality across much larger areas as compared to other related studies.


Utility Maximized Two-level Game-Theoretic Approach for Bandwidth Allocation in Heterogeneous Radio Access Networks

Abstract

        To solve the optimal bandwidth allocation problem in heterogeneous radio access networks (H-RANs), a two-level game-theoretic approach is proposed to maximize the utility of network providers and users by considering the network resource distributions and service demands. In the area-level game, the Nash equilibrium is achieved by using a noncooperative game for the bandwidth allocation in different areas to maximize the network utility. Moreover, in each service area, the traffic-level bandwidth allocation and pricing approach is applied to allocate the appropriate portion of bandwidth for different networks and users using the Stackelberg game, with network as the leader and user as the follower. Furthermore, the Stackelberg equilibrium is achieved by an iterative algorithm in this paper. Simulation results prove that the proposed game-theoretic approaches can efficiently and significantly maximize the utility of networks and users.


Potential of Network Energy Saving through Handover in HetNets

Abstract

We propose a handover strategy that helps to minimize energy consumption at base stations (BSs) in heterogeneous networks (HetNets). In order to conserve energy at BSs, the proposed strategy is to uniquely consider energy consumption generated during the handover execution phase under an expectation on the stochastic behavior of handover parameters, in making a handover decision. This plays a critical role in eliminating unnecessary handovers occurring within the areas covered by multiple BSs. In order to verify the energy saving potential of the proposed strategy, we formulate a handover problem as a constrained Markov Decision Process (CMDP), where the objective is to minimize expected total energy consumption at BSs in serving a traffic flow and the constraints are a diverse set of delay requirements for supporting multiple traffic classes. Simulation results show the achievable performance gain through the proposed strategy from the perspective of energy efficiency.


Distributed Energy-Saving Cellular Network Management Using Message-Passing

Abstract

 This paper presents a distributed energy-saving management strategy for green cellular networks. During offpeak periods, an energy-saving operation is activated. A subset of base stations (BSs) in the network enters an energy-saving state, i.e., switched-off mode, while satisfying traffic demands without discontinuity of user services. To this end, the remaining operating BSs should compensate for the coverage holes by taking over the responsibility of user service. Such a scenario can be formulated into a combinatorial optimization that maximizes the overall energy-saving of the network. To address this computationally demanding task, we develop a distributed algorithm that provides an efficient solution by using a state-of-the-art technique based on a message-passing framework. The simulation results confirm considerable energy-saving gains over previously existing techniques and prove the viability for this strategy for self-organizing green cellular networks.

Handover Count Based Velocity Estimation and Mobility State Detection in Dense HetNets

Abstract

In wireless cellular networks with densely deployed base stations, knowing the velocities of mobile devices is key to avoiding call drops and improving the quality of service to the user equipments (UEs). A simple and efficient way to estimate a UE’s velocity is by counting the number of handovers made by the UE during a predefined time window. Indeed, handover-count based mobility state detection has been standardized since long term evolution (LTE) Release-8 specifications. The increasing density of small cells in wireless networks can help in accurate estimation of velocity and mobility state of a UE. In this paper, we model densely deployed small cells using stochastic geometry, and then analyze the statistics of the number of handovers as a function of UE velocity, small-cell density, and handover count measurement time window. Using these statistics, we derive approximations to the Cramer–Rao lower bound (CRLB) for the velocity estimate of a UE. Also, we determine a minimum variance unbiased (MVU) velocity estimator whose variance tightly matches with the CRLB. Using this velocity estimator, we formulate the problem of detecting the mobility state of a UE as low, medium, or high-mobility, as in LTE specifications. Subsequently, we derive the probability of correctly detecting the mobility state of a UE. Finally, we evaluate the accuracy of the velocity estimator under more realistic scenarios such as clustered deployment of small cells, random way point (RWP) mobility model for UEs, and variable UE velocity. Our analysis shows that the accuracy of velocity estimation and mobility state detection increases with increasing small cell density and with increasing handover count measurement time window.


QoS-aware dynamic MAP selection in HMIPv6 architecture

Abstract

The main problem dealt with in this paper is the creation of a protocol for improved QoS-aware mobility management support in cellular all-IP networks, whereby we propose a new algorithm for QoS-aware mobility management, based on multidimensional QoS metrics. An analytical framework for performance evaluation was presented as well. The proposed algorithm for QoS-aware dynamic MAP selection relies on multidimensional QoS metrics, defined in QoS-preference spaces of the mobile node and QoS-ability spaces of MAP candidates, in the decision-making process. The metric is chosen to achieve the desired QoS level through three parameters: bandwidth, delay, and reliability, while retaining the balance of MAP’s loads in the entire network. For purposes of performance evaluation of the proposed model, we used: algorithm convergence, traffic class distribution by MAP’s, and handover delay. Results showed that the standard deviation for each component of the QoS-ability vector is two orders of magnitude smaller than the deviation in the static MAP selection scenario. We achieved a total handover delay decrease from 20 ms to several hundred milliseconds, by simplifying DAD procedures preserving the simplicity of architecture.


Handover schemes in heterogeneous LTE networks: challenges and opportunities

Abstract

     For satisfying rapidly increasing data rates at hotspots and enhancing coverage in buildings, small cells, such as femtocells, picocells, and microcells, are deployed in LTE-A. Femtocells are typically installed at hotspots and overlay with the macrocell to improve energy efficiency and data rates. In macro-femto HetNets, the handover issue is more important than that in macrocell networks. On one hand, more frequent handovers are triggered because the coverage range of a femtocell is small, and multiple femtocells are overlaid. On the other hand, some schemes, such as load balancing, aimed at improving network performance, will also cause frequent handover in macro-femto HetNets. Therefore, handover has a significant impact on the performance of macro-femto HetNets. In this article, we first survey the state-of-the-art handover techniques that are aimed at keeping ongoing connections uninterrupted or ensuring the quality of service of mobile users. Then we introduce load-balance-related handover schemes. Moreover, an energy-efficient handover scheme is presented. At last, we point out interesting research issues on handover schemes in macro-femto HetNets.


A Probabilistic Threshold-Based Bandwidth Sharing Policy for Wireless Multirate Loss Networks

Abstract

      We propose a probabilistic bandwidth sharing policy, based on the threshold (TH) policy, for a single cell of fixed capacity in a homogeneous wireless cellular network. The cell accommodates random input-traffic originated from K service-classes. We distinguish call requests to new and handover, and therefore, the cell supports 2K types of arrivals. If the number of in-service calls (new or handover) of a service-class exceeds a threshold (different for new and handover calls of a service-class), a new or handover arriving call of the same service-class is not always blocked, as it happens in the TH policy, but it is accepted in the system with a predefined state-dependent probability. The cell is analyzed as a multirate loss system, via a reversible continuous-time Markov chain, which leads to a product form solution (PFS) for the steady state distribution. Thanks to the PFS, the calculation of performance measures is accurate, but complex. To reduce the computational complexity, we determine performance measures via a convolution algorithm.


Cognitive Cellular Networks: A Q-Learning Framework for Self-Organizing Networks

Abstract

   Self-organizing networks (SON) aim at simplifying network management (NM) and  optimizing network capital and operational expenditure through automation. Most SON functions (SFs) are rule-based control structures, which evaluate metrics and decide actions based on a set of rules. These rigid structures are, however, very complex to design since rules must be derived for each SF in each possible scenario. In practice, rules only support generic behavior, which cannot respond to the specific scenarios in each network or cell. Moreover, SON coordination becomes very complicated with such varied control structures. In this paper, we propose to advance SON toward cognitive cellular networks (CCN) by adding cognition that enables the SFs to independently learn the required optimal configurations. We propose a generalized Q-learning framework for the CCN functions and show how the framework fits to a general SF control loop. We then apply this framework to two functions on mobility robustness optimization (MRO) and mobility load balancing (MLB). Our results show that the MRO function learns to optimize handover performance while the MLB function learns to distribute instantaneous load among cells.

Clustering Depth Based Routing for Underwater Wireless Sensor Networks

Abstract

   Large propagation delay, high error rate, low band-width and limited energy in Underwater Sensor Networks (UWSNs) attract the attention of most researchers. In UWSNs, efficient utilization of energy is one of the major issue, as the replacement of energy sources in such environment is very expensive. In this paper, we have proposed a Cluster Depth Based Routing (cDBR) that is based on existing Depth Based Routing (DBR) protocol. In DBR, routing is based on the depth of the sensor nodes: the nodes having less depth are used as a forward nodes and consumes more energy as compared to the rest of nodes. As a result, nodes nearer to sink dies first because of more load. In cDBR, cluster based approach is used. In order to minimize the energy consumption, load among all the nodes are distributed equally. The energy consumption of each node is equally utilized as each node has equal probability to be selected as a Cluster Head (CH). This improves the stability period of network from DBR. In cDBR Cluster Heads (CHs) are used for forwarding packets that maximizes throughput of the network. We have compared our results with DBR and Energy Efficient DBR (EEDBR). The simulation result validates that cDBR achieves better stability period and high throughput comparatively to DBR and EEDBR.


Optimal Relay Selection and Power Control with Quality-of-Service Provisioning in Wireless Body Area Networks

Abstract

    A game-theoretic approach is proposed to investigate the problem of relay selection and power control with quality of service constraints in multiple-access wireless body area networks (WBANs). Each sensor node seeks a strategy that ensures the optimal energy efficiency and, at the same time, provides a guaranteed upper bound on the end-to-end packet delay and jitter. The existence of Nash equilibrium for the proposed noncooperative game is proved, the Nash power control solution is analytically calculated, and a distributed algorithm is provided that converges to a Nash relay selection solution. The game theoretic analysis is then employed in an IEEE 802.15.6-based WBAN to gauge the validity and effectiveness of the proposed framework. Performance behaviors in terms of energy efficiency and end-toend delay and jitter are examined for various scenarios. Results demonstrate the merits of the proposed framework, particularly for moving WBANs under severe fading conditions.


Enabling Interference-Aware and Energy-Efficient Coexistence of Multiple Wireless Body Area Networks with Unknown Dynamics

Abstract

        This paper presents an adaptive interference mitigation scheme for multiple coexisting wireless body area networks (WBANs) based on social interaction. The proposed scheme considers the mobility of nodes within each WBAN as well as the relative movement of WBANs with respect to each other. With respect to these mobile scenarios traffic load, signal strength, and the density of sensors in a WBAN are incorporated to optimize transmission time with synchronous and parallel transmissions to significantly reduce the radio interference and energy consumption of nodes. This approach leads to higher packet delivery ratio (PDR) and longer network lifetime even with nodes dynamically moving into and out of each others interference region. We make channel assignment more energy-efficient and further reduce power consumption using transmit power control with simple channel prediction. Simulation results show that our approach maintains optimum spatial reuse with a range of channel dynamics within, and between, coexisting BANs. This protocol based on social interaction is shown to mitigate interference and minimize power consumption, and increase the spatial reuse and PDR of each WBAN, while increasing network lifetime. In the context of the adaptive interference mitigation scheme proposed, this paper also reviews the state of the art in literature on mobility, MAC layer, and power control solutions for WBANs, as well as providing a summary of interference mitigation schemes previously applied for the coexistence of WBANs.


Efficient Certificateless Access Control for Wireless Body Area Networks

Abstract

      Wireless body area networks (WBANs) are expected to act as an important role in monitoring the health information and creating a highly reliable ubiquitous healthcare system. Since the data collected by the WBANs are used to diagnose and treat, only authorized users can access these data. Therefore, it is important to design an access control scheme that can authorize, authenticate and revoke a user to access the WBANs. In this paper, we first give an efficient certificateless signcryption scheme and then design an access control scheme for the WBANs using the given signcryption. Our scheme achieves confidentiality, integrity, authentication, non-repudiation, public verifiability and ciphertext authenticity. Compared with existing three access control schemes using signcryption, our scheme has the least computational cost and energy consumption for the controller. In addition, our scheme has neither key escrow nor public key certificates since it is based on certificateless cryptography.


Three-dimensional geographic routing in wireless mobile ad hoc and sensor networks

Abstract

      Geographic routing has been considered as an attractive approach for wireless mobile ad hoc and sensor networks due to its effectiveness and scalability. Over the past few decades, numerous geographic routing protocols have been proposed extensively in 2D space. However, these protocols are no longer valid if ad hoc or sensor networks are distributed in 3D environments, such as space, atmosphere, and ocean. Because of the surprisingly difficulty of designing geographic routing protocols for 3D networks compared to 2D networks, only a few prior studies have focused on 3D geographic routing specifically designed for ad hoc and sensor networks. In this article, we first illustrate the principles of 3D geographic routing, and categorize current research work based on different criteria. Then we compare the 3DGR protocols under study through comprehensive analysis. Finally, we point out the open issues and opportunities for further research.


A Novel Energy Efficient Object Detection and Image Transmission Approach for Wireless Multimedia Sensor Networks

Abstract

       Energy efficient object detection and image transmission are one of the key issues in Wireless Multimedia Sensor Networks (WMSN). Recent approaches inWMSN propose in-node object detection and tracking algorithms. However, a little effort has been made to effectively detect object presence and absence in images in WMSN. Since object detection has a direct relation with image transmission, therefore effective object detection algorithm would provide a reduction in false transmission of image information. In this paper, a novel object presence model and image transmission scheme is proposed for WMSN. This scheme uses the transmission of an image segments rather than a complete image. It guarantees innode energy conservation and minimal image content transmission to the sink node. The proposed scheme is evaluated based on in-node energy consumption and reconstructed image Peak Signal to Noise Ratio (PSNR). Simulation results show that the proposed approach saves 95% of the node energy with the received image PSNR of 46 db as compared to other state of the art approaches.


Energy-Efficient Localization and Tracking of Mobile Devices in Wireless Sensor Networks

Abstract

   Wireless sensor networks (WSNs) are effective for locating and tracking people and objects in various industrial environments. Since energy consumption is critical to prolonging the lifespan of WSNs, we propose an energy efficient LOcalization and Tracking (eLOT) system, using low-cost and portable hardware to enable highly accurate tracking of targets. Various fingerprint-based approaches for localization and tracking are implemented in eLOT. In order to achieve high energy efficiency, a network-level scheme coordinating collision and interference is proposed. On the other hand, based on the location information, mobile devices in eLOT can quickly associate with the specific channel in a given area, while saving energy through avoiding unnecessary transmission. Finally, a platform based on TI CC2530 and the Linux operating system is built to demonstrate the effectiveness of our proposed scheme in terms of localization accuracy and energy efficiency.


An Rethinking Cognitive Access for Underwater Acoustic Communications

Abstract

           In this paper, we investigate how to reformulate the concepts of cognitive access, originally developed for radio communications, in the framework of underwater acoustic communications. A straightforward application of the classical energydetection- based cognitive approach, such as the one employed for radio communications, would result in a reduced spectrum utilization in an acoustic scenario. Actually, in the underwater scenario, acoustic signals sensed by a network node are likely to be due to communication sources as well as natural/artificial acoustic sources (e.g., mammals, ship engines, and so forth), differently from classical cognitive radio access, where each signal at the receiver is generated by a communication source. To maximize the access probability for cognitive acoustic nodes, we focus on understanding the nature of sensed interference. Toward this aim, we try to discriminate among natural and communications sources by classifying the images representing the time and frequency features of the received signals, obtained by means of theWigner– Ville transform. Two different classifiers are considered here. The first one is targeted on finding natural interference while the second one looks for communication. Simulation results show how the herein described approach drastically enhances the access probability in an acoustic scenario with respect to a direct rephrasing of classical cognitive access. A possible protocol for implementing cognitive access is also described and its performance evaluated.


Bio-inspired cybersecurity for wireless sensor networks

Abstract

        Rapid advances in information and communication technologies have led to the emergence of cyber-physical systems (CPSs). Wireless sensor networks (WSNs) play a pivotal role in CPSs, particularly for operations such as surveillance and monitoring. However, these WSNs are subject to various types of cyberattacks that can cause damage, theft, or destruction of sensitive data, in addition to disruption of services provided by CPSs. To strengthen cybersecurity in WSN-enabled CPSs, various researchers have proposed a new category of efficient algorithms, inspired by biological phenomena. We present a careful review of different bio-inspired techniques developed for improving cybersecurity of CPSs using WSNs. Additionally, we propose a generic bio-inspired model called Swarm Intelligence for WSN Cybersecurity (SIWC) that addresses drawbacks of prior bio-inspired approaches.


Auction-Based Data Gathering Scheme for Wireless Sensor Networks

Abstract

          This paper proposes a novel data gathering scheme for Wireless Sensor Networks (WSN) that limits the energy expenditure, and hence, prolongs network lifetime. Data gathering is modeled as an auction where a node broadcasts its own result only if it is higher than the maximum already-broadcasted result by other nodes. For a WSN of 100 nodes, mathematical and simulation results show that the proposed scheme can save up to 70% of the energy consumption with less than 1% performance loss, compared to the conventional scheme.


Optimal Routing for Lifetime Maximization of Wireless Sensor Networks with a Mobile Source Node

Abstract

       We study the problem of routing in sensor networks where the goal is to maximize the network’s lifetime. Previous work has considered this problem for fixed-topology networks. Here, we add mobility to the source node, which requires a new definition of the network lifetime. In particular, we redefine lifetime to be the time until the source node depletes its energy. When the mobile node’s trajectory is unknown in advance, we formulate three versions of an optimal control problem aiming at this lifetime maximization. We show that in all cases, the solution can be reduced to a sequence of Non-Linear Programming (NLP) problems solved on line as the source node trajectory evolves. When the mobile node’s trajectory is known in advance, we formulate an optimal control problem which, in this case, requires an explicit off-line numerical solution. We include simulation examples to illustrate our results.


Page 1 of 212
RECENT PAPERS