In dynamic applications, carrier frequency offset (CFO) may lead to performance degradation in LoRa communication. Especially for the fractional CFO part, energy leakage and peak reduction in the demodulation domain pose additional challenges for accurate symbol detection after noise superimposition. Therefore, we propose a phase rotation based CFO estimation and compensation (PREC) method to achieve accurate fractional CFO estimation by supplementing frequency offset to an integer value to promote energy concentration in demodulation and improve noise resistance. By continuous phase rotations, a frequency offset is first added to the original CFO in the preamble of the received signal to form an integer value. After the integer CFO estimation in peak detection, the introduced frequency offset can be subtracted from the estimated integer value to obtain the original CFO, which helps compensate for the CFO in the data symbol for accurate detection. Monte Carlo simulation results demonstrate that the PREC exhibits robustness to CFO and helps to obtain better bit error rate (BER) performance with fewer upchirps in the preamble.

Ambient backscatter communication (AmBC) is a newly cutting-edge technology for the Internet of Things, which utilizes the ambient radio frequency signal as the carrier to transmit information. Existing works focus on the simple on-off keying modulation which has low channel utilization. However, it is not desirable to develop the high-order modulation in the power domain due to the weak strength of the backscattered signal. In this paper, we extend the high-order modulation in the time domain instead, i.e., high-order time shift keying (TSK). Since the channel coherent time is unknown at the receiver and the tag, the detection methods with training symbols will have a huge performance degradation if the channels are changed and training symbols become outdated. To overcome this challenge, we further propose the transition-aided TSK (TA-TSK) modulation and the frequency-shifting TSK (FS-TSK) modulation, which do not need to send training symbols at the tag. These two methods can work well even if the channel coherent time is as short as one time slot. Meanwhile, the detection methods for TSK are developed and the corresponding closed-form bit-error-rate (BER) expressions are obtained. Simulation results show that a high modulation order is more suitable for the M-ary phase shift keying source than the complex Gaussian source. The high order TSK can provide at least$2~dB$signal-to-noise ratio (SNR) gain at the same BER compared to the on-off keying.

In this paper, signal detection in ambient backscatter communication (AmBC) with dynamic sources is investigated. Dynamic ambient radio frequency sources are the sources which transmit their signals randomly. The states of the dynamic source include the on-state and off-state. In the off-state, dynamic sources do not transmit any signal and the backscatter device cannot backscatter any signal accordingly. The received signals may contain only pure noises. Thus, it brings new challenges to the signal detection. We develop detection methods with/without truncating received signals for AmBC under both dynamic complex Gaussian source signals and dynamic M-ary phase-shift keying (M-PSK) source signals with on-off keying. To make detection simple and tractable for the dynamic M-PSK at the receiver, Manchester code is applied. Simulation results show it is necessary to truncate part of the signal in detections in the high signal-to-noise ratio region unless the received signals contain few pure noise samples, in order to reduce the bit-error-rate of backscatter signals.

Backscatter communication (BackCom) technology that takes advantage of the radio frequency signals to facilitate the communications of passive devices has attracted much attention in recent years. To enhance its communication performance, the multiple-input and multiple-output (MIMO) technology has been introduced to BackCom. Channel estimation is crucial for the MIMO BackCom system. However, the optimization for the pilot training sequences has not been studied. In this paper, we propose a pilot sequence design algorithm for MIMO BackCom systems with spatially correlated antennas, which can estimate both the direct link and the backscatter link channel information. We derive the optimal structure of the source and the tag pilot sequences which achieves the minimum mean-squared error (MSE) of channel estimation. Then, we optimize the power allocation between the source pilot sequences. Simulation results show that our proposed algorithm can estimate channel efficiently and achieve better sum MSE performance than the benchmark without power allocation.

The propagation delay is non-negligible in underwater acoustic networks (UANs) since the propagation speed is five orders of magnitude smaller than the speed of light. In this case, space and time factors are strongly coupled to determine the collisions of packet transmissions. To this end, this paper analyzes the impact of space-time coupling on slotted medium access control (MAC). We find that a sending node has specific location-dependent interference slots and slot-dependent interference regions. Thus, the collisions may span multiple slots, leading to both inter-slot and intra-slot collisions. Interestingly, the slot-dependent interference regions could be annulus inside the whole transmission range. It is pointed out that collision-free regions exist when a guard interval in a slot is larger than a packet duration. In this sense, the long slot brings spatial reuse within the transmission range. We then derive the closed-form expressions for the successful transmission probability of Slotted-ALOHA and the upper-bound and the lower-bound for the successful transmission probability in UANs. We further find that the optimal guard interval to reach the peak successful transmission probabilities is not larger than a packet duration, which is much shorter than the existing slot setting in the typical Slotted-ALOHA in the uniformly distributed UANs. Simulation results verify our findings, and also show that the performance of vertical transmissions is more sensitive to the spatial impact than horizontal transmissions in UANs.

In recent years, reliable and timely data collection from underwater acoustic sensor networks (UASNs) has attracted widespread attention in academia. For this proposal, we study the autonomous underwater vehicle (AUV)-based real-time mobile relaying network in UASNs. Relay placement determines the reliability of communication in the relay network. We first formulate the relay positions that can meet a certain frame error rate (FER) requirement as the FER-restricted area (FRA), and approximate the FRA with a three-dimensional ellipsoid mathematical formula. The problem of reliable and timely data collection becomes planning a short AUV relaying trajectory under the different-sized FRA constraints. To this end, we propose a nearest-community (N-C) trajectory planning algorithm and further propose a member grouping method to form communities. Simulation results verify that the approximate FRA is more than 90% consistent with the real FRA and show that the proposed N-C can successfully receive more packets per minute and consume fewer sensors' energy than other algorithms.

Ultrasonic intra-body communication (IBC) is an emerging technique to enable revolutionary healthcare applications. This paper proposes an ultrasonic index modulation with spread spectrum (UsIM-SS) scheme for IBCs. The information bits consist of modulation bits and index bits. The core idea of UsIM-SS is that the modulation bits are spread by spreading codes and the index bits are leveraged to select the time-hoping sequence to transmit the spreading bits. We also find that the existing ultrasonic wideband (UsWB) and UsIM can be regarded as special cases of the proposed UsIM-SS. Furthermore, the soft decision-based maximum likelihood (S-ML) receiver and hard decision-based ML (H-ML) receiver are proposed for UsIM-SS, providing a trade-off between bit-error rate (BER) and complexity. Theoretical spectrum efficiency (SE) and BER expressions for UsIM-SS using S-ML and H-ML are also derived and verified by extensive Monte Carlo simulations. Both theoretical and simulation results show that UsIM-SS can achieve lower BER and higher SE than existing UsWB and Us 1M techniques.

The ocean contains abundant resources and has high-scientific and economic values. Increasing underwater communication scenarios stimulates many research efforts on underwater wireless ad hoc networks. Underwater acoustic communication technology supports medium to long-range wireless communication. However, it suffers from low-transmission data rates and long link delays. This article introduces an acoustic-radio cooperation network (ARCNet) model for more efficient maritime information transmission. Under this ARCNet, we propose a new radio-acoustic opportunistic hybrid (RAOH) routing protocol composed of a neighbor discovery mechanism and a hybrid routing strategy. In the neighbor discovery stage, we explore the surface radio links to aid in selecting the shortest delay path between an underwater node and the surface nodes. In the route establishment stage, we combine the advantages of opportunistic routing and on-demand routing and design an opportunistic hybrid routing strategy, which improves the success rate of data forwarding and reduces the time spent on route establishment. Simulation results show that the proposed RAOH protocol outperforms traditional terrestrial and underwater routing strategies in routing response speed, packer delivery rate, throughput, end-to-end delay, and energy efficiency.

Relaying is an effective method to achieve reliable and timely data collection, which is one of the most important parts of underwater acoustic sensor networks (UASNs). Considering that the relay position determines the reliability of relay communication, we study the problem of relay placement and place an autonomous underwater vehicle (AUV) to mobile relay data transmission and realize the reliable, low-latency, and low-energy data collection in UASNs. First, we formulate the relay positions that can meet a certain frame error rate (FER) requirement as the FER-restricted area (FRA), and approximate FRA with a three-dimensional geometry formula. The problem of reliable and timely data collection becomes planning a short AUV relaying trajectory under the FRA constraint. To this end, we propose a nearest-community (N-C) trajectory planning algorithm to design the AUV relay trajectory. A member grouping method and the necessity of position (NoP) concept are proposed to further reduce the relay positions and trajectory length of the AUV. Simulation results verify that the approximate FRA is more than 90% consistent with the real FRA and show that the N-C using NoP-based member grouping can successfully receive more packets per minute and consume fewer sensors’ energy than other algorithms.

In this paper, we analyse the achievable rate and capacity for ambient backscatter communications with dynamic sources under the binary input and signal output (BISO) channel. Dynamic sources are the sources that transmit signals to the air intermittently, rather than continuously transmitting signals like static sources. Instead of assuming static ambient sources, we investigate the dynamic sources. Due to the complexity of the expression of mutual information, we resort to the numerical simulation results for the BISO channel capacity and the capacity-achieving distribution is obtained by one-dimensional searching. We utilize inequality to show the relationship between static sources and dynamic sources in terms of the achievable rate and capacity. The numerical studies show that the maximal of the mutual information of the BISO channel is not achieved by a uniform input distribution, and the mutual information and capacity of the BISO channel with dynamic sources are close to that of the BISO channel with static sources, scaled by the probability that the dynamic source is in the on-state.

Marine science and Internet of Underwater Things applications rely significantly on collecting data from underwater sensors. Data collection using long-distance underwater acoustic communications consumes a lot of energy in underwater sensor nodes, which are powered by batteries. To achieve low-energy consumption, we can use the autonomous underwater vehicle (AUV) to move close to sensor nodes and exploit the short-range and high-rate communications. Most of the existing AUV-based data collection schemes consider the scenarios having the knowledge of node positions, where the cruising trajectory can be computed before the AUV’s departure. These schemes cannot apply to some scenarios such as turtle tracking for a certain sea area having no position information. To this end, we first propose a planning-while-detecting approach to dynamically detect the sensors on turtles and adjust the AUV cruising direction to collect data. To further improve data efficiency under the energy limit of the AUV, we group the sensors that can share the same trajectory using their detected directions. A grouping-based dynamic trajectory planning (GDTP) is then proposed to determine the next cruising direction that can visit the group of sensors having the largest amount of data and demanding the least cruising energy at the risk of detection errors. Simulation results show that GDTP achieves significantly higher data collection efficiency than the existing trajectory planning algorithms in dynamic scenarios, and as the communication range increases, it can even outperform the existing algorithms with node locations.

This paper studies virtual machine (VM) scheduling in a queueing cloud computing system with stochastical arrivals of heterogeneous jobs by considering jobs’ delay requirements. The delay-optimal VM scheduling in such a cloud computing system is formulated as a multi-resource multi-class problem minimize the average job completion time, which is often NP-hard. To solve such a problem, we first propose a queueing model that buffers the same type of VM jobs in one virtual queue. The queueing model then divides the VM scheduling into two parallel low-complexity algorithms, i.e., intra-queue buffering and inter-queue scheduling. A min-min best fit (MM-BF) policy is used to schedule the jobs in different queues to minimize the remaining system resources, while a shortest-job-first (SJF) policy is used to buffer the job requests in each queue based on their job lengths in an ascending order. To avoid job starvation for the long-duration jobs in SJF-MMBF, we further propose a queue-length-based MaxWeight (QMW) policy based on Lyapunov drift to minimize the queue lengths of VM jobs, which is called SJF-QMW. Simulation results show that, SJF-MMBF and SJF-QMW achieve low delay performance in terms of average job completion time and high throughput performance in terms of job hosting ratio.

Ocean big data is becoming a future trend of the Internet of Underwater Things (IoUT). Underwater wireless sensor networks (UWSNs) technique is a promising method to realize ocean big data. However, the limited energy and the low location accuracy of sensor nodes make the data collection of UWSNs difficult. To reduce the energy consumption of sensor nodes, we consider an autonomous underwater vehicle (AUV) based data collection, where the AUV moves close to the sensors to collect data using short-range high-rate communications. Particularly, we propose a grouping-based dynamic trajectory planning (GDTP) for the AUV. GDTP does not require the position information of sensor nodes. It dynamically detects the existence and directions of sensor nodes, based on which the detected sensor nodes are grouped by a proposed common communication area model. The cruising direction of the AUV is dynamically determined with the maximum expected payoff that considers the data collection and energy consumption in inaccurate detection. Simulation results show that the proposed GDTP collects more data packets with less energy compared to the existing schemes.

The long propagation delay presents a nonnegligible impact on medium access control (MAC) in underwater acoustic networks (UANs), leading to the coupling of spatial propagation delay and transmission time in determining frame collisions at receivers. To this end, this article first reveals the space-time coupling relationship for collision-free transmissions. We find that the interference region for simultaneous transmissions is an annulus, which is much smaller than the whole transmission range. Thus, UANs have more spatial multiplexing opportunities than terrestrial radio networks. By deriving the collision probabilities of the ALOHA-based protocols, we show that the annulus interference region in UANs provides potential to improve random access protocols. We further derive the heuristics for optimal scheduling for periodical transmissions under space-time coupling, which is formulated as a mixed-integer linear programming problem. The obtained heuristics are then used to design low-complexity algorithms, which are verified by simulation results. Our proposed algorithms can achieve the optimal scheduling in star networks and outperform the existing MAC in mesh networks.

Ultrasonic intra-body communication (IBC) is a promising enabling technology for future healthcare applications, due to low attenuation and medical safety of ultrasonic waves for the human body. A splitting receiver, referred to as the splitting-detection separate-decision (SDSD) receiver, is introduced for ultrasonic pulse-based IBCs, and SDSD can significantly improve bit-error rate (BER) performance over the traditional coherent-detection (CD) and energy detection (ED) receivers. To overcome the high complexity and improve the BER performance of SDSD, a splitting-detection joint-decision (SDJD) receiver is proposed. The core idea of SDJD is to split the received signal into two streams that can be separately processed by CD and ED, and then summed up as joint decision variables to achieve diversity combining. The theoretical channel capacity and BER of the SDSD and SDJD are derived for M-ary pulse position modulation ( M-PPM) and PPM with spreading codes. The derivation takes into account the channel noise, intra-body channel fading, and channel estimation error. Simulation results verify the theoretical analysis and show that both SDSD and SDJD can achieve higher channel capacity and lower BER than the CD and ED receivers with perfect channel estimation, while SDJD can achieve the lowest BER with imperfect channel estimation.

Intra-body communication (IBC) plays a significant role in future health care systems. This article proposes a pulse-based ultrasonic index modulation (UsIM) to enable high data rate and low power IBCs. Two grouping-based UsIM strategies, namely ultrasonic grouped-chip indexed modulation (UsCIM) and ultrasonic chip-group indexed modulation (UsGIM), are proposed to implement multiple access for intra-body networks (IBNs). UsCIM uses the chip indices in a group to convey additional information bits for a biosensor node, while UsGIM uses the group indices and each node occupies one chip in a group. Three receivers, including the maximum likelihood (ML), the maximum ratio combination (MRC), and the energy detection (ED) receivers, are then proposed and studied. The theoretical bit-error rate (BER) upper bound for the ML receiver, and theoretical BER expressions for the MRC and ED receivers are derived. The system performance metrics in terms of complexity, throughput and energy efficiency are analyzed. The analytical and simulation results reveal that UsGIM achieves a lower BER than UsCIM under channel fading and multipath effect. Both UsCIM and UsGIM achieve higher throughput and energy efficiency than the existing IBC systems, and both strategies are important for energy-constrained implanted IBNs.

Ultrasonic intra-body communication (IBC) is a promising enabling technology for many future healthcare applications, due to the low attenuation and medical safety of ultrasonic waves for human body. This paper proposes a splitting-detection joint-decision (SDJD) receiver structure for ultrasonic pulse-based IBCs. The core idea is to split the received signal into two steams for the coherent-detection (CD) and energy detection (ED), and then the two detection outputs are summed up as joint decision variables to estimate the received signals. The theoretical channel capacity and bit-error rate (BER) expressions are derived, considering receive noise, tissue attenuation, and channel fading in intra-body channels. Both simulation and theoretical results show that the SDJD receiver outperforms the pure CD, ED, and splitting-detection separate-decision receivers in terms of BER performance. In addition, the proposed receiver has a low complexity requirement on the hardware and decision algorithm. Therefore, SDJD is an important structure to receive signals correctly and efficiently for ultrasonic IBCs.

Resource control is a branch of congestion control in wireless sensor networks (WSNs). The congestion-oriented resource control schemes in WSNs often re-route traffic loads to avoid the congested areas. The re-routing overhead for congestion avoidance is particularly large in large networks. Thus, this kind of congestion control lacks of strong scalability. In this paper, a congestion control based on a geographic routing algorithm (CcbGr) for WSNs is proposed. It determines the congestion status of a node using thresholds of the queue length and the changing rate of the queue length. We adopt a geographic forwarding scheme in avoiding congestion. A new algorithm of distance update is also proposed to by-pass routing holes which are common for geographic routing. When a node is on the edge of a hole, it will increase the distance between itself and the sink according to specific rules. Simulation results show that CcbGr can effectively address network congestion with high scalability.

Internet of Underwater Things (IoUT) is regarded as the network of interconnected smart underwater objects that enables marine monitoring and exploration. Underwater acoustic sensor networks (UASNs) is an ideal network infrastructure to support IoUT. Usually, network-level analysis requires a simplified model for channel interference. Existing research on UASNs usually applies channel interference models from terrestrial wireless networks. Its effectiveness is questionable. In this article, we investigate two specific features of acoustic channels and propose a new channel interference model. First, based on the fact that acoustic signals have inconsistent transmission range in vertical and horizontal directions, we propose an ellipsoid sensing model and derive the probability that a communication pair will have an effective link. Second, we investigate the receiving time inconsistency in time-slotted acoustic networks and derive the analytical probability of collision-free transmission. Finally, a new protocol model is proposed to describe the acoustic channel interference. Its theoretical throughput, in terms of delivery rate, is also presented. Numerical examples illustrate the effectiveness of the theoretical analysis.

With the advance of the Internet of Underwater Things, underwater acoustic sensor network (UASN) has been considered as a promising technology for oceanic engineering to explore and exploit marine resources. Due to the time variability, frequency selectivity, and the very limited available bandwidth, underwater acoustic (UWA) channels are generally known as one of the most challenging communication media in use today. The highly dynamic nature of UWA links calls for adaptive, scalable, and efficient routing schemes for UASNs. Depth-based routing has attracted much attention because it can work efficiently without the need for full-dimensional location information of sensors. However, it suffers from the problems of void region and detouring forwarding. To this end, this paper proposes a distance-vector-based opportunistic routing (DVOR) scheme to address these problems. DVOR uses a query mechanism to establish the distance vectors for UWA nodes, which record the smallest hop counts toward the sink. Then, an opportunistic routing is developed to coordinate the packet forwarding based on the distance vectors. DVOR has a low signaling overhead in opportunistic forwarding, as well as the ability to avoid the problems of void region and long detour. Simulation results show that DVOR outperforms the existing routing protocols in terms of packet delivery ratio, energy-efficiency, and average end-to-end delay.

Visual tracking, which has been widely used in many vision fields, has been one of the most active research topics in computer vision in recent years. However, there are still challenges in visual tracking, such as illumination change, object occlusion, and appearance deformation. To overcome these difficulties, a reliable point assignment (RPA) algorithm based on wavelet transform is proposed. The reliable points are obtained by searching the location that holds local maximal wavelet coefficients. Since the local maximal wavelet coefficients indicate high variation in the image, the reliable points are robust against image noise, illumination change, and appearance deformation. Moreover, a Kalman filter is applied to the detection step to speed up the detection processing and reduce false detection. Finally, the proposed RPA is integrated into the tracking-learning-detection (TLD) framework with the Kalman filter, which not only improves the tracking precision, but also reduces the false detections. Experimental results showed that the new framework outperforms TLD and kernelized correlation filters with respect to precision, f-measure, and average overlap in percent.

The redundant design and dynamic nature of traffic raise an energy inefficiency issue in communication networks. We exploit the selfishness of both users and the network to schedule cooperatively the idle links and nodes into sleep to save energy. We first formulate the sleep scheduling problem from a perspective of routing, and then propose a greedy algorithm to solve the problem. To reduce the complexity of centralized computation, we further propose a user-network cooperation-based mechanism, where the network publishes a proportionally weighted cost-sharing rule related to energy consumption, while the users selfishly choose their routes with the least cost accordingly. The proposed cooperation mechanism attracts users to aggregate their traffic on fewer links and nodes. The network then simply puts the idle links and nodes into sleep. Selfish routing behaviors are modeled by an α-approximate routing game, where the α factor is adopted to consider the energy consumption, packet losses, and delay during re-routing. We prove the equilibrium existence, convergence, and convergence speed of the best responses, and evaluate the lower bound performance in terms of price of anarchy with further improvement by an advertisement method. Distributed algorithms based on the best responses are also developed to implement the cooperative mechanism. Simulation results over network instants from SNDlib show that our game-based algorithms outperform the greedy and heuristic centralized algorithms in saving energy.

Frequency domain pilot multiplexing technique (FDPMT) has recently emerged as an appealing technique for channel estimation in single-carrier frequency domain equalization systems as it achieves high spectral efficiency at the expense of tiny signal distortion. In FDPMT, pilot positions are dynamically selected to achieve a relatively low level of signal distortion, rendering pilot position detection (PPD) indispensable at the receiver. In this letter, by exploiting both the prior known pilots and the statistical information of the data tones, we propose a novel PPD scheme based on the maximum-likelihood criterion. Simulation results show that our proposed PPD scheme significantly improves the PPD accuracy and outperforms the existing counterparts in terms of bit error rate.

In this paper, we propose a cross-layer design framework for transmitting Markov modulated Poisson process (MMPP) traffic over Nakagami-m fading channel with delay demands. The adaptive modulation and coding (AMC) technique is used at the physical layer. The energy efficiency is described as the average throughput over the average transmission power, where both of throughput and transmit power have full consideration of the queuing system. We first derive the closed-form expressions of the delay and the energy efficiency with the stationary distribution of the system. We then derive the energy efficient thresholds to choose the AMC transmission modes. At last, we derive the transmission policy to maximize the energy efficiency with delay constraints. Numerical results are provided to support the theoretical development.

Quality of Service (QoS) provisioning for wireless multimedia applications has attracted increasing research attentions. Due to limited bandwidth of time-varying wireless links and various QoS requirements of bursty multimedia applications, an effective call admission control (CAC) policy that efficiently utilizes the bandwidth is desired. We have proposed a Differentiated Queueing Service (DQS) to support per-packet differentiated services for admitted traffic, which has been illustrated feasibility for mixed streams with various QoS requirements in multimedia applications. This paper furthers the research by exploring a DQS-based CAC policy to maximize the bandwidth utilization of wireless networks while provisioning call-level as well as packet-level QoSs for multimedia applications. To this end, we novelly use delay bound violation probability as admission control threshold and a basic DQS-based CAC policy, namely Delay Bound violation probability Guard (DBG) policy is proposed. Then we optimize the policy by formulating it as a Semi-Markov Decision Process (SMDP). The optimal DBG policy is found by solving the linear programming formulation problem. Numerical results show that, the DBG policy over SMDP achieves its goals of maximizing the bandwidth utilization while provisioning both call-level and packet-level differentiated QoSs for each class of multimedia calls in comparison with bandwidth guard CAC policies.

When energy efficiency is considered, cooperative communications may not be appealing since it involves multiple nodes in transmissions. To exploit the benefits of distributed user cooperation in cooperative wireless ad hoc networks (WANETs), we propose a distributed energy-efficient selective diversity (EESD) topology control to improve energy efficiency in the network, which jointly considers network capacity and energy consumption in terms of bits per Joule. EESD forms transmission coalitions via cooperative manner (i.e, diversity) selections, by taking into account the cost of channel information exchange. We then formulate EESD as a coalition game and propose an adaptive coalition formation algorithm for EESD with proved convergence property and stable coalition structures. Simulation results show the performance improvement of EESD in energy efficiency compared to the existing topology control schemes.

Users in secondary multi-hop networks have to select paths and obtain spectrum for the selected paths to deliver their traffic. We consider a case that users obtain TV white spaces from a geo-location database for their selected paths at some cost. To reduce the cost of routing, users will reuse their spectrum along the paths. Due to link diversity, redundant spectrum exists on some links. To further improve spectrum utilization in the network, we propose a Nash bargaining based spectrum sharing rule for users to share and reduce redundant spectrum. Analytical and simulation results show that our proposal reduces the total spectrum requirement in the network.

Due to the scarce bandwidth and limited power supply in mobile terminals, performance and energy consumption in wireless communications must be continually focused upon. Cooperative transmissions improve the capacity performance of wireless ad hoc networks (WANETs). However, when energy efficiency is considered, the advantage of user cooperation over non-cooperation does not always exist since it involves multiple nodes with more energy consumption in transmissions. To exploit the benefits of user cooperation in cooperative WANETs, we propose a distributed energy-efficient selective diversity (EESD) topology control to improve energy efficiency, which jointly considers network capacity and energy consumption in terms of bits per Joule. EESD forms transmission coalitions via cooperative manner (i.e, diversity) selection, by taking into account the cost of channel information exchange. We then formulate EESD as a coalition game and propose an adaptive coalition formation algorithm for EESD with proved convergence property and stable coalition structures. Simulation results show the performance improvement of EESD in energy efficiency and network lifetime compared to the existing topology control schemes.

Owing to the absence of intermediate energy storage elements, the outputs of matrix converters are influenced easily by abnormal input voltages. To this end, this paper proposes a generalized indirect space vector modulation with feedforward compensation capacity based on the space vector representation of the switch-state transfer matrices. To simplify the calculation of the modulation duty-cycle matrix, both the singular value decomposition and the space vector modulation techniques are used to render clear physical meanings to the modulation process, and to synthesize the desired variables without complex computations. Using this approach, the duty cycles for switch combinations can be calculated online to decouple the low order harmonics in the input side from the outputs. Besides, the geometrical perspective provided by this method makes the optimization on the switching sequence convenient. The simulation results verify the validity of the proposed method in maintaining the outputs sinusoidal and balanced disregarding the abnormal input voltages.

Quality of Service (QoS) provisioning especially delay guarantee is particularly important to the increasing popularity of mobile wireless multimedia applications. Traditional QoS schemes include the per-flow Integrated Service (IntServ) and per-class Differentiated Service (DiffServ) as well as their variants. To overcome the scalability problem of IntServ and the coarse QoS granularity of DiffServ, the per-packet Differentiated Queueing Service (DQS) was proposed. This paper focuses on analytically modeling of the QoS provisioning performance of DQS for mobile wireless multimedia applications. In the analysis, arriving packets of a session are classified into different streams according to their delay requirements. Each stream is described by a modified Fractional Brownian Motion (FBM)based traffic model, where both the traffic self-similarity of multimedia streams and heavy-tailed distribution of packet sizes are considered. We develop a stochastic service capability model for mobile wireless links. Then a delay guarantee model for DQS is further developed by deriving the delay bound violation probability and packet loss rate. Computer simulations are conducted to validate the proposed analytical models.

Reduction of energy consumption is becoming a major concern in communication networks, because of its potential economical benefits and environmental impact. In this paper, we propose a prediction-based traffic aggregation scheme to realize energy saving for green networking. By predicting the traffic loads, the scheme aggregates and reroutes the traffic in some nodes to make them idle and then sleep in the next control time interval when the traffic is predicted light. Simulation results demonstrate that the proposed scheme can yield the accuracy of traffic prediction with a small normalized one-step prediction error and significant energy saving corresponding to the traffic loads in a simple network. Meanwhile, the throughput and delay of the network are almost not affected by the scheme.

Cooperative communication can significantly enhance transmission reliability and bandwidth efficiency in wireless networks. Most existing works are focused on physical layer issues of cooperative communications. Many upper layer aspects of cooperative communications merit further research. In this paper, we investigate its impacts on network topology and capacity. Specifically, we present a novel Capacity-Optimized COoperative (COCO) topology control scheme for mobile ad hoc networks (MANETs) with cooperative communications. We consider both upper layer network capacity and physical layer relay selections in the proposed scheme. In addition, only the channel estimate, not the perfect channel status, is assumed to be known in our scheme. The topology control problem in MANETs is formulated as a discrete stochastic optimization problem, which can be solved using a stochastic approximation approach. Simulation results are presented to show the effectiveness of the proposed scheme.

Security is an important issue in mobile ad hoc networks (MANETs). However, security schemes have significant impacts on throughput. That is because 1) they need some overhead and consume some network resources, thus decrease throughput consequently, 2) most previous works consider security and throughput separately in designing a MANET, which can not achieve an overall optimization of network performance. In this paper, we propose a topology control scheme to improve throughput by jointly designing upper layer security schemes and physical layer schemes related to channel conditions and relay selections for cooperative communications. Simulation results show that our scheme can substantially improve throughput in MANETs.

Cooperative communications can significantly enhance transmission reliability and bandwidth efficiency in wireless networks. However, many upper layer aspects of cooperative communications merit further research. In this paper, we investigate its impacts on network topology and network capacity, which is determined by considerable aspects, such as physical layer capacity, interference, path length, etc. Since cooperative communications enhance physical layer capacity and relay selection impacts network topology directly, we present a Capacity-Optimized COoperative (COCO) topology control scheme for mobile ad hoc networks (MANETs) with cooperative communications. We consider both upper layer network capacity and physical layer relay selections in the proposed scheme. In addition, only the channel estimate, not the perfect channel status, is assumed to be known in our scheme. The topology control problem in MANETs is then formulated as a discrete stochastic optimization problem, which can be solved using a stochastic approximation approach. Further, an improved COCO is presented to reconfigure network topology to track the changing mobile environment dynamically. Simulation results are presented to show the effectiveness of the proposed scheme.

Devices in wireless multihop networks should communicate with each other along minimum energy-consuming paths for energy-saving. To this end, any energy-aware topology control scheme should not destroy these paths. The Localized Dijkstra Topology Control (LDTC) algorithm discussed in this paper aims at constructing a network topology to preserve all the minimum energy paths by using only local information subject to network connectivity. It also takes link quality requirements to assign transmission power. Furthermore, LDTC is proven analytically that it can maintain bidirectional wireless links to facilitate handshake mechanisms. Moreover, within the Euclidean graph, no two links intersect in the derived topology, which can mitigate interference. Simulation results indicate that LDTC generates a simple topology with small node degree and short transmission radius. When cooperating with different routing strategies, it performs well in energy and hop count spanners, and can be easily implemented in wireless multihop networks.

Denial-of-Service (DoS) attacks especially distributed DoS (DDoS) attacks have become significant and increasing threats to the Internet. Huge efforts from both academia and industry have been made on detection and defense of DDoS attacks. However, most detection and defense schemes do not directly aim at protecting the victim of attacks itself (e.g., servers) but attack sources or intermediate network units. Although locating and identifying attacking sources are critical to stop attacks and for legal procedure, rapid and efficient predicting DDoS attacks to happen in the server is more important to reduce damage caused by attacks and even prevent attacks from happening. However, this part has not been addressed sufficiently in the literature. In this paper, we first briefly review research efforts on DDoS attacks, and then discuss a method to define and quantify attacks to severs based on available service rates. This is because the server is often the direct victim of DDoS attacks and the one-point failure of the entire service system. No matter whether there are attacks undergoing, if a sever is overloaded even by normal service requests, the effect imposed to a service system is equivalent to that of attacks. A prediction method for the available service rate of the protected server is then proposed, which applies the Auto Regressive Integrated Auto Regressive (ARIMA) model. Finally, we investigate the proposed prediction method to predict DDoS attacks through simulation studies with NS2. The simulation results show that the prediction algorithm is effective to predict most attacks.