Unmanned Aerial Vehicle base stations (UBSs) are able to assist a cellular IoT network to provide content delivery service for ground users. This paper studies the UBS deployment problem in a cache-enabled UBS-assisted cellular network. The UBS deployment problem is first formulated as a joint optimization problem with an objective to minimize the average content delivery delay of all users in a service area. We decompose the problem into three sub-problems: content caching deployment, position deployment, and BS association, and propose a multi-agent proximal policy optimization (MAPPO)-based UBS deployment algorithm to solve the sub-problems. Specifically, the proposed algorithm uses edge agents on UBSs and a two-layer MAPPO algorithm including a cache layer and a position layer to solve the content caching deployment sub-problem and the UBS position deployment sub-problem. Meanwhile, it uses a central agent in a core network and a proximal policy optimization (PPO) algorithm to solve the BS association sub-problem. Simulation results demonstrate that the proposed UBS deployment algorithm can significantly improve the network performance in terms of the average content delivery delay and the cache hit ratio of all users in the network.

Mobility performance analyses of base station (BS) cooperation have an important role in the design of BS cooperation strategies for mobile UAVs in a cellular-connected UAV network (CCUN) for Internet of Things applications. This paper presents an in-depth study on the mobility performance analyses of a mobile UAV under BS cooperation in a CCUN. Performance models are derived for analyzing the mobility performance of a mobile UAV under BS cooperation in terms of the handover rate, handover probability, coverage probability, and average throughput, taking into account the occurrence of a handover. In deriving the performance models, modified 2-D and 3-D random waypoint (RWP) mobility models are used to describe the random or uncertain flight trajectories of a mobile UAV during a task execution. Based on the derived performance models, the impacts of main network parameters on the mobility performance of a mobile UAV under BS cooperation are investigated, which can help determine reference values for the network parameters in the design of a BS cooperation strategy for a mobile UAV. The difference between the mobility performance of a mobile UAV under a 2-D RWP model and that under a 3-D RWP model is investigated, which reveals several useful results for the design of BS cooperation strategies for mobile UAVs.

Uncrewed aerial vehicle base stations (UBSs) can be used to assist a cellular Internet of Things (IoT) network to provide content delivery service for ground users. This article studies the UBS deployment problem in a cellular IoT network for content delivery and formulates the problem as a joint mixed-integer linear programming problem with an objective to minimize the average content delivery delay of all users in a service area in a time frame. The formulated problem is decomposed into three subproblems: a content caching deployment problem, a UBS position deployment problem, and a BS association problem. A deep reinforcement learning-based UBS deployment (DRL-UD) algorithm is proposed to solve the problem. In the DRL-UD algorithm, an Informer-based user pattern prediction algorithm is introduced to predict the content request pattern and mobility pattern of users. Based on the prediction of user patterns, a two-layer proximal policy optimization (TLPPO)-based UBS deployment algorithm is introduced to solve the three subproblems using a cache layer, a position layer, and an implicit enumeration method, respectively. Simulation results show that the proposed DRL-UD algorithm can significantly reduce the average content delivery delay and increase the cache hit ratio of all users in the network.

This paper proposes an enhanced resource selection (eInP-RS) scheme for efficient intra-platoon message delivery of cooperative awareness messages (CAMs) and decentralized environmental notification messages (DENMs). To achieve this goal, the eInP-RS scheme allows each vehicle to transmit CAM and DENM packets on a C-V2X channel (CH1) and an 802.11p channel (CH2), separately, and introduces four mechanisms to enhance the standardized sensing-based SPS scheme. A contention window (CW) size adjustment mechanism is introduced to enable a vehicle to adjust its CW size according to the information it collects on CH1 in order to avoid potential packet collisions on CH2; a resource partition mechanism is introduced to divide frequency-time resources in a selection window into two sets in order for vehicles moving in opposite directions to select different resources and thus avoid potential merging collisions on CH1; an intra-platoon cooperation mechanism is introduced to enable the PL of a platoon to know the resource and channel occupation information of the platoon's hidden nodes on CH1 and CH2; and a packet collision detection mechanism is used to enable a non-platoon vehicle to detect packet collisions occurring on both channels after a lane-changing maneuver to avoid potential merging collisions. Simulation results show that the proposed eInP-RS scheme outperforms the standardized sensing-based SPS scheme in terms of the intra-platoon CAM/DENM delivery ratio and the average intra-platoon DENM delivery delay.

This paper investigates the vehicle to vehicle (V2V) task offloading problem in a vehicular network with multiple radio access technologies (RATs). The problem is formulated as a mixed integer nonlinear programming problem (MINP) with an objective to minimize the average offloading delay of all offloading tasks in the network by optimizing a set of offloading decisions subject to delay and computing resource constraints. To solve the formulated problem, a log-sum-exp approximation is used to transfer the MINP problem to a combinatorial optimization problem to obtain a probability distribution of all possible offloading decisions. Based on the probability distribution, a Markov-chain based method is used to obtain the state transition probabilities between different offloading decisions. Based on the state transition probabilities, a V2V task offloading decision (V2V-TOD) algorithm is proposed to make offloading decisions via using a learning process consisting of three stages: SV and RAT selection, sub-channel selection, computing resource allocation. Simulation results show that the proposed V2V-TOD algorithm can efficiently reduce the average offloading delay as compared with three benchmark algorithms.

This article proposes an aerial and ground base station cooperation (AG_CoMP) strategy for improving the downlink transmission performance of an aerial UAV user (AUE) in an unmanned aerial vehicle and cellular integrated network. The AG_CoMP strategy allows both a ground base station (GBS) and an aerial base station (ABS) to participate in BS cooperation, and seeks to reduce the negative impact of BS cooperation on the overall network performance. The GBS and ABS closest to an AUE are associated to cooperatively provide downlink transmission of the AUE and a blanking area is defined to disallow nearby GBSs to transmit during the transmission of the AUE. A combination of noncoherent joint transmission and dynamic point blanking is used to reduce the large signaling overhead for transmission of the AUE’s data between the cooperative BSs with the proposed strategy. Performance models are derived to analyze the downlink transmission performance of an AUE in terms of the coverage probability, achievable throughput, and SINR meta distribution of the AUE. Numerical results show that compared with three benchmark strategies, the proposed AG_CoMP strategy can ensure a relatively good downlink transmission performance, and achieve a better downlink transmission performance when an AUE is at an intermediate altitude between an ABS and a GBS.

This article proposes a cellular vehicle-to-everything (C-V2X) mode 4 and 802.11p-based resource selection (eInP-RS) scheme for efficient intraplatoon message delivery. The proposed eInP-RS scheme is intended to improve the delivery performance of cooperative awareness messages (CAMs) and decentralized environmental notification messages (DENMs) within a platoon. To achieve this goal, it allows each vehicle to transmit CAM and DENM packets on a C-V2X channel (CH1) and an 802.11p channel (CH2), separately, and introduces four mechanisms to enhance the standardized sensing-based semi-persistent scheduling (SPS) scheme. A contention window (CW) size adjustment mechanism is introduced to enable a vehicle to adjust its CW size according to the information it collects on CH1 in order to avoid potential packet collisions on CH2; a resource partition mechanism is introduced to divide frequency-time resources in a selection window into two sets in order for vehicles moving in opposite directions to select different resources and thus avoid potential merging collisions on CH1; an intraplatoon cooperation mechanism is introduced to enable a platoon leader to know the resource and channel occupation information of the platoon’s hidden nodes on CH1 and CH2; and a packet collision detection mechanism is used to enable a nonplatoon vehicle to detect packet collisions occurring on both channels after a lane-changing maneuver to avoid potential merging collisions. Simulation results show that the proposed eInP-RS scheme outperforms the standardized sensing-based SPS scheme in terms of the CAM/DENM delivery ratio and average DENM delivery delay of a platoon vehicle.

Metaverse can provide powerful human-centric interactive experiences for users and metaverse application content is the fundamental component that supports the metaverse. Considering that metaverse users are more sensitive to the delay of content delivery service, unmanned aerial vehicles (UAV) can be used as aerial base stations (BSs) to assist a cellular network to provide better content delivery service for delay-sensitive metaverse users as UAV base stations (UBSs) have big potential for line-of-sight (LoS) transmission and can be deployed closer to metaverse users than macro base stations (MBSs). This paper studies the content delivery performance analysis of a cache-enabled UBS-assisted cellular network for metaverse users. Analytical models are derived for investigating the content delivery performance of the network in terms of the content delivery success probability of the network and the average content delivery delay of a metaverse user. In deriving the analytical models, a more realistic repulsive point process is considered for modeling the location distribution of MBSs, and an air-to-ground (A2G) channel model with both a LoS link and a non-line-of-sight (NLoS) link is considered. Moreover, the cache hit probability of a UBS using a probabilistic caching strategy is also taken into consideration. A BS association strategy for delay-sensitive metaverse users based on the strongest average received power at a user and the cache hit probability of a UBS is proposed. In addition, the association probabilities with the association strategy are derived for different types of base stations. A lower bound of the content delivery success probability and an upper bound of the average content delivery delay are obtained based on the derived analytical models. The numerical results justify the effectiveness and advantage of the proposed BS association strategy and show that there exist an optimal UBS height and an optimal value of the number of UBSs, which result in the optimal content delivery performance. The obtained results can provide theoretical guidance for the deployment of UBSs in a cache-enabled UBS-assisted cellular network to provide better human-centric content delivery service for metaverse user.

This paper studies the UAV trajectory planning (UtraP) problem in a UAV-enabled data collection (DC) system. The problem is formulated as an optimization problem aiming to minimize a UAV's mission completion time, taking into account the time constraint of data and the mitigation of interference on ground users. To solve the formulated problem, we decompose the problem into two sub-problems: the UAV's visiting sequence optimization and the UAV's hovering position optimization, and propose a genetic algorithm based algorithm and a successive convex approximation based algorithm to solve the two sub-problems, respectively. Moreover, an interference-aware trajectory optimization algorithm is proposed to solve the main problem by iteratively optimizing the UAV's visiting sequence and hovering positions. Simulation results demonstrate the efficiency of the proposed trajectory optimization algorithm.

Broadcasting is an important routing strategy for message delivery in a VANET. However, the broadcast storm caused by loads of duplicate data packets can be severe, which leads to intense contention among vehicle nodes and a large number of dropped packets. In this case, the efficiency of message delivery is limited. This paper considers a broadcast routing protocol specially designed for urban areas with dense traffic and a huge amount of data traffic, and proposes a Preferred-Zone based broadcast (PreZcast) protocol. To alleviate the broadcast storm and improve the efficiency of message delivery, PreZcast only allows neighbors located within a Preferred Zone (PreZ) of a source node to rebroadcast a received packet. The PreZ is chosen based on the initial location and the distribution of two-hop neighbors of the source node. To further validate PreZcast in a more realistic and irregular topology, we evaluate PreZcast with real datasets issued from Bologna road traffic. Moreover, MultiPoint Relay (MPR) is introduced to further reduce duplicate packets in the network. Simulation results show that as compared with the Flooding protocol, PreZcast approximately improves 15% of broadcast efficiency and reduces 65% of packets transmitted with 5% more energy consumption, while PreZcast with MPR improves 10% of broadcast efficiency and reduces 70% of packets transmitted with only 0.5% more energy consumption.

This paper studies the joint computing offloading and resource allocation problem in an MEC network. We formulate the problem as an optimization problem with the objective to maximize the network throughput while satisfying the delay requirements of as many service requests as possible. Meanwhile, we propose a deep-Q-network (DQN) based joint computing offloading and resource allocation (D-CORAL) algorithm to solve the formulated problem. The proposed D-CORAL algorithm attempts to jointly optimize edge node selection, and spectrum resource and computing resource allocation for each service request by learning online to better adapt to a dynamic environment. Simulation results show the proposed algorithm can achieve larger network throughput than two benchmark algorithms.

This paper analyzes the coverage performance of a backhaul-limited UBS-assisted cellular network and focuses on the downlink coverage probability of the network, taking into account the effects of both access links and backhaul links. Based on stochastic geometry, a theoretical model is derived to build the relationship between the downlink coverage probability and relevant system parameters. In deriving the model, the distributions of UAV base stations (UBSs) and macro-base stations (MBSs) are modeled as two independent Poisson point processes (PPPs). For access links and backhaul links, both line-of-sight (LoS) links and non-line-of-sight (NLoS) links are considered. Moreover, an association strategy in which a user selects a BS providing the largest average received power for connection is considered. The derived theoretical model is validated through simulation results. Based on numerical results, the impacts of relevant system parameters and UAV parameters on the coverage probability of the network are investigated. The results indicate that backhaul links have a big impact on the coverage performance of the network and are expected to be guaranteed. Moreover, the derived theoretical model can be used to provide a theoretical basis for the deployment of UBSs

This paper considers the spectrum resource and computing resource allocation in an mobile edge computing (MEC) network. The problem is formulated as an optimization problem with an objective to maximize the average successful processing ratio of users’ service requests while satisfying users’ service delay requirements. To solve the formulated problem, a federated learning (FL) and deep-Q-network (DQN) based cooperative resource allocation (CoRA) algorithm is proposed to adaptively select optimal offloading nodes and allocate spectrum and computing resources for users’ service requests by training DQNs in a master node and slave nodes in the form of federated learning. Simulation results show that the proposed CoRA algorithm outperforms two benchmark algorithms in terms of the average successful processing ratio.

New radio-based access to unlicensed bands (NR-U) multichannel access procedures are 3GPP standards for supporting 5G NR-U and WiFi coexistence in unlicensed frequency bands. NR-U provides a new solution to alleviating the severe spectrum scarcity problem of 5G cellular networks in licensed frequency bands for supporting diverse Internet of Things (IoT) services. This article studies the performance modeling of an NR-U and WiFi coexistence system using the NR-U Type B multichannel access procedure and the WiFi DCF procedure and focuses on a system model with two unlicensed channels: 1) a primary channel and 2) a secondary channel. 2-D Markov models are first built to describe the NR-U category-4 LBT procedure and the WiFi DCF procedure on the primary channel, respectively. A 3-D Markov model is then built to describe the WiFi DCF procedure on the secondary channel, taking into account the effect of the NR-U Type B multichannel access procedure. Based on the built Markov models, performance models are further derived for analyzing the system performances in terms of system throughput and packet delay on the primary channel and/or secondary channel. The derived performance models are validated through simulation results. The impacts of the number of NR-U gNBs/WiFi APs and major system parameters on the system performances are investigated based on the derived performance models.

This article studies the performance modeling of an NR-U and WiFi coexistence system using the NR-U category-4 listen-before-talk (LBT) procedure and 802.11e enhanced distributed channel access (EDCA) mechanism in the presence of hidden nodes. 3-D Markov models are first developed to describe the NR-U category-4 LBT procedure for a priority-${p}$queue of an NR-U gNB and the 802.11e EDCA mechanism for an AC-${q}$queue of a WiFi AP, taking into account the effects of hidden nodes and the transmission of different-priority data. Based on the Markov models, theoretical models are derived for analyzing the mean throughput of a priority-${p}$queue in an NR-U gNB and that of an AC-${q}$queue in a WiFi AP as well as the mean transmission delay of an NR-U priority-${p}$data packet and that of a WiFi AC-${q}$data packet. The derived theoretical performance models are validated through simulation results. Based on the performance models, the impacts of major system parameters on the system performance are investigated, including the density of NR-U gNBs, the sensing distance and channel transmission rate of an NR-U gNB, and the data packet arrival rate at an NR-U gNB.

Unmanned Aerial Vehicle base stations (UBSs) can be used to assist a ground cellular network to enhance its network services for cellular users. This paper studies the coverage performance analysis of a cache-enabled UBS-assisted cellular network. Analytical models are derived for investigating the overall coverage probability of the network and the average achievable rate of a cellular user. In deriving the analytical models, an air-to-ground (A2G) channel model with both a line-of-sight (LoS) link and a non-line-of-sight (NLoS) link, a BS association strategy based on the strongest average received power, and a cache model with a probabilistic caching strategy are considered. The cache hit probability of UBSs based on the cache model is also taken into consideration. Moreover, the association probabilities with the association strategy are derived for different types of base stations. The derived analytical models are validated through simulation results and the impacts of system parameters on the coverage performance of the network are investigated through numerical results. Compared with existing relevant work, the novelty of this work is that the effects of both an access link and a backhaul link are taken into account in the coverage performance analysis. The obtained results can provide theoretical guidance for the deployment of UBSs in a cache-enabled UBS-assisted cellular network.

This paper studies the joint spectrum and computing resource allocation problem in a mobile edge computing (MEC) network, where mobile users can offload computing tasks to an edge node for processing. We formulate the joint resource allocation problem as an optimization problem with an objective to maximize the system throughput while satisfying the service delay requirements of as many service requests as possible. A novel DQN-based resource allocation algorithm is proposed to solve the formulated problem. The proposed algorithm consists of a resource pre-allocation process and a resource allocation process. The former performs joint spectrum and computing resource allocation for each service request based on a deep-Q-network (DQN) model, which attempts to make an optimal decision on resource allocation for each service request by learning online to better adapt to dynamic traffic arrivals and diverse service requirements. The latter performs resource allocation for each service request according to a resource pre-allocation decision and service requirements. Simulation results show that the proposed DQN-based resource allocation algorithm outperforms three benchmark algorithms in terms of system throughput.

This paper studies the initial contention window (CW) size adjustment problem in an NR-U and WiFi coexistence network with an NR-U system and a WiFi system. The problem is formulated as an initial CW size optimization problem with an objective to adaptively find an optimal initial CW size such that the throughput of the NR-U system is maximized while the throughput of the WiFi system is guaranteed. To solve the problem, a Deep Recurrent Q-Network (DRQN) based initial CW size optimization algorithm is proposed to adaptively find an optimal CW size by training the main DQN in a DRQN and observing the current status of the coexistence network, including the current CW size, the throughput of the WiFi system, the throughput of the NR-U system, and the number of NR-U users that have data to transmit. Simulation results show that the proposed DRQN-based CW optimization algorithm outperforms a fixed CW mechanism and an adaptive CW mechanism in terms of the throughput of the NR-U system and the fairness of the coexistence network.

This paper studies the UAV trajectory planning problem in a UAV monitoring system and considers a typical data collection scenario, where a UAV is dispatched to a geographical area to collect time-constrained data in a set of monitoring areas and transmit collected data to a ground base station (GBS). We formulate the UAV trajectory planning problem as an optimization problem with the objective to minimize the UAV's mission completion time by jointly optimizing the UAV's waypoints and the duration the UAV stays in each line segment, taking into account the age of information (AoI) of data in the monitoring area, and the on-board energy of the UAV. To solve the problem, we propose a successive convex approximation (SCA) based optimization algorithm. Simulation results show that the proposed trajectory optimization algorithm can find a better solution to the problem than a benchmark algorithm.

In this paper, a theoretical model is derived to analyze the intra-platoon message delivery delay (InP-delay) for an autonomous platoon to enable each platoon member to receive a message transmitted from the platoon leader via multi-hop unicast. Both platoon vehicles and non-platoon vehicles employ a carrier sense multiple access (CSMA/CA) mechanism to contend for channel access. Since the delay of delivering a message within a platoon may affect the platoon stability, the effects of major system parameters on the InP-delay are investigated, including the platoon size, the intra-platoon spacing, the density of non-platoon vehicles, the transmission range and carrier sensing range of vehicles. This work is of help to the stability maintenance of an autonomous platoon by providing reference values for system parameter configuration under different use case requirements.

This paper considers the network resource allocation problem in multi-service MEC networks, and in particular considers joint spectrum and computing resource allocation in an edge network service system. The resource allocation problem under consideration is formulated as an optimization problem, which takes into account users’ minimum service requirements in terms of the service delay, transmission rate, and computation rate, with an objective to minimize the average service delay of a user’s service request. To solve the problem, we propose a deep-Q-network (DQN) based joint spectrum and computing resource allocation algorithm, which attempts to optimize the resource allocation for each service request by learning a more efficient allocation scheme so as to better adapt to dynamic traffic arrivals, diverse service requirements, and complex environmental conditions. Simulation results show that the proposed resource allocation algorithm can efficiently reduce the average service delay compared with two benchmark algorithms.

This article studies the unmanned aerial vehicle (UAV) trajectory planning problem in a UAV-enabled environmental monitoring system and considers a typical data collection scenario where a UAV is dispatched to a geographical area to collect time-constrained data in a set of monitoring areas and transmit collected data to a ground base station (GBS). We formulate the UAV trajectory planning problem as an optimization problem with the objective to minimize the UAV’s mission completion time by jointly optimizing the UAV’s flying speeds, hovering positions, and visiting sequence, taking into account the Age of Information (AoI) of data in monitoring areas, and the on-board energy of the UAV. To solve the problem, we decompose the formulated optimization problem into two subproblems: a UAV speed optimization problem and a UAV path optimization problem, and propose successive convex approximation (SCA) method-based and generic algorithm (GA)-based algorithms to solve the subproblems. Based on the proposed algorithms, we further propose an AoI-and-energy-aware trajectory optimization (AoI-EaTO) algorithm to solve the main problem. Simulation results show that the proposed AoI-EaTO algorithm can find a better solution to the problem than two benchmark algorithms. Moreover, given the UAV’s on-board energy and maximum speed as well as the positions of the GBS and monitoring areas, the AoI limitation threshold that the system is able to satisfy can be obtained through simulation results. This threshold can be used to decide if the UAV is able to finish a particular data collection mission, which is useful to the deployment of the mission.

Local delay is a main component of data transmission delay in a mobile-edge computing (MEC)-based vehicularad hocnetwork (VANET). To reduce the local delay, there is a need for investigating the impacts of underlying network parameters on the local delay through performance modeling and analysis in order to provide a guideline for the deployment of edge nodes. This article considers the local delay problem in an MEC-based VANET and focuses on the local delay analysis in a suburban scenario where a vehicle node driving on a street needs the computing service from an edge node. To this end, a closed-form analytical model is derived for the uplink local delay and downlink local delay, respectively, based on stochastic geometry. In deriving the analytical model, it is assumed that vehicle nodes on each street are distributed following an independent 1-D homogeneous Poisson point process (PPP), and the edge nodes are deployed at each intersection with a certain probability. Meanwhile, carrier sense multiple access (CSMA) is employed by both vehicle and edge nodes in channel access, which is different from those considered in most existing local delay models for VANETs. Based on the derived analytical model, the impacts of underlying network parameters on the local delay are investigated through numerical results. The derived local delay model can be used to provide a guideline for the deployment of edge nodes.

This paper studies the power allocation problem in device-to-device (D2D) communications underlaying cellular networks. A Q-learning based distributed power allocation (Q-PA) algorithm is first proposed, which attempts to obtain optimal power allocation through iterative updating of Q-value tables. Based on the Q-PA algorithm, a neural network (NN) based power allocation (N-PA) algorithm is further proposed, in which training data obtained using the Q-PA algorithm are used to train an NN model, and the trained NN model is used to perform power allocation for D2D users. Simulation results show that both the Q-PA algorithm and the N-PA algorithm can improve the system throughput as compared with two traditional algorithms. The N-PA algorithm can significantly reduce the time cost for power allocation at a small expense of the system throughput as compared to the Q-PA algorithm.

Unmanned Aerial Vehicles (UAV) deployed in the air can be used as base stations (BSs) to provide uplink and downlink transmissions for ground cellular users in a target area. This paper considers the coverage recovery problem in a cellular network with UAVs as base stations when one or more UAVs go offline due to energy replenishment or extreme environment. A coverage analysis of the target area is first presented in order to obtain the condition on the coverage radius of a UAV BS (UBS) for producing a full coverage of the target area. In the case of UAV offline, the coverage radius of a UBS needs to be adjusted to recover the full coverage of the target area, which can be implemented by adjusting either the altitude or the transmission power of the UBS. Based on the obtained full coverage condition on the UBS coverage radius, a coverage recovery analysis is further presented for determining the adjustment range of the altitude or transmission power of a UBS. For this purpose, the relationship between the coverage radius and the altitude and that between the coverage radius and the transmission power are analyzed. Through numerical results, it is demonstrated that the full coverage of a target area can effectively be recovered by adjusting either the altitude or transmission power of a UBS in the case of UBS offline.

Batch forwarding using intra-flow random linear network coding (RLNC) has been used to improve the performance of a wireless network constituent of lossy links. However, existing batch-based forwarding mechanisms in this aspect can lead to a lot of bandwidth waste and thus reduced transmission efficiency. In this paper, we design a Sliding WIndow based Multiple batch forwarding mechanism (SWIM) using RLNC. In SWIM, multiple batches are allowed to be sent out simultaneously in a way that the forwarding process is managed by a sliding window. In SWIM, adaptive rate assignment is used to assign bandwidth resources to different batches based on their decoding states at the destination, in order to make full use of the bandwidth resources. Simulation results show that SWIM can achieve improved throughput performance as compared with existing work.

The autonomous underwater vehicle(AUV)-aided localization techniques for underwater acoustic devices show promising applications in many scenarios, and most researches in this area are based on the time of arrival (ToA) or the time difference of arrival (TDoA) measurements. However, these measurements are not readily available. To develop a more universally applicable scheme, we investigate the possibility of employing the Doppler shift measurements for underwater localization of acoustic devices in this paper. To be specific, we employ a low-complexity algorithm for Doppler estimation, and prove that the estimation error can be well approximated by zero-mean Gaussian distribution. Based on the Doppler estimates, we can obtain a series of nonlinear equations. To solve them, we propose a two-phase linear algorithm to obtain high-accuracy position information of the target devices. Compared with the conventional iterative algorithms, the proposed one does not require initial estimate. Both the closed-form localization error and the Cramér-Rao lower bound are presented. They prove to be consistent for reasonably small Doppler estimation error. Besides, we conduct simulations to verify the theoretical analysis. Moreover, the complexity of the proposed algorithm only grows linearly with the number of Doppler shift measurements.

This paper studies the component carrier selection (CCS) problem in an LTE-U and WiFi coexistence network, taking into account the throughput requirements of LTE-U users. Two types of LTE-U users are considered, i.e., type-1 users with minimum throughput requirement and type-2 users without minimum throughput requirement. The CCS problem is formulated as an optimization problem with the objective to select an optimal CC among all CCs for each LTE-U user, either type-1 or type- 2, such that the throughput of each type-2 user on the selected CC is maximized while the minimum throughput requirement of both the type-1 users and WiFi users are guaranteed. An enhanced carrier sense adaptive transmission (CSAT-e) mechanism is first presented to coordinate the transmission of LTE-U type-1 users, LTE-U type-2 users and WiFi users on the same component carrier. Based on that, a heuristic QoS-guaranteed CCS (Q-CCS) algorithm is proposed for solving the formulated CCS problem. Simulation results are shown to evaluate the performance of the proposed Q-CCS algorithm in terms of the system utility.

This paper studies the analysis of the uplink local delay in a mobile edge computing (MEC) based vehicular ad hoc network (VANET). An analytical model is first derived using stochastic geometry to analyze the average uplink local delay of a service requesting vehicle to transmit a packet to the nearest edge node on a highway road. In the derived model, the spatial distribution of vehicle nodes on each lane and that of edge nodes are modeled as an independent one-dimensional homogeneous Poisson point process (PPP), respectively. A nearest- receiver model is used to determine the edge node for accommodating the service request of a vehicle node. The derived analytical model is validated through simulation results and the impacts of major parameters on the average uplink local delay are investigated.

This paper studies the downlink spectrum resource allocation problem in a small cell network and focuses on mitigating intra-tier interference between different small cells. A two-tier clustering based downlink resource allocation (TCRA) algorithm is proposed to perform spectrum resource allocation. To increase spectrum utilization, the algorithm allows different small-cell user equipments (SUEs) to share the same physical resource blocks (PRBs). Meanwhile, to mitigate intra-tier interference, those SUEs who are close to each other in distance are avoided to share the same PRBs as much as possible. To implement this, a two-tier clustering approach is introduced in PRB allocation. In the first tier, all small cells in the system are partitioned into a set of small cell clusters based on graph coloring, and those small cells with the same color are partitioned into the same cluster. In the second tier, all SUEs in each small cell cluster are further partitioned into a set of user clusters based on the interference graph of each small cell cluster. After the two-tier clustering, PRB allocation is performed based on the user clusters obtained. Simulation results show that the proposed TCRA algorithm can significantly improve the system performance in terms of total system capacity.

This paper considers the component carrier selection (CCS) problem for an LTE-U and WiFi coexistence network operating in an unlicensed spectrum band. An unlicensed component carrier selection (U-CCS) algorithm is proposed to select component carriers (CCs) for newly arrived LTE-U users. To support the U-CCS algorithm, an implementation of the carrier sense adaptive transmission (CSAT) mechanism is first presented to coordinate the transmission of LTE-U users and WiFi users on a single CC. The proposed U-CCS algorithm takes both carrier load and channel condition into account and aims to find a CC among all CCs at the arrival of a new LTE-U user, on which the user can achieve the maximum throughput, while ensuring that the average throughput of each WiFi user on the same CC meets the minimum throughput requirement. Simulation experiments show that the proposed U-CCS algorithm outperforms a couple of existing CC selection algorithms in terms of the minimum LTE-U user throughput, minimum WiFi user throughput, and newly arrived user throughput.

This paper proposes a battery and data rate aware resource allocation (BARA) algorithm for quality of experience (QoE) provisioning in D2D communication underlaying cellular networks. The BARA algorithm takes into account the data rate and battery consumption of a user device in spectrum resource allocation, and aims to improve the average QoE of D2D pairs in the system. To this end, the Peukert's law is introduced as a battery model to predict the battery consumption of a user's device. A new QoE model is defined based on the typical MOS model, taking into account a logistic function, to measure the QoE of a user in the system. Moreover, a satisfaction degree function is introduced to decide each D2D user's priority in resource allocation. Simulation results demonstrate that the BARA algorithm can significantly improve the average QoE of D2D pairs in the network without obviously affecting the overall throughput of the system.

This paper proposes an adaptive vehicular MAC protocol, called A-VeMAC, for VANETs. A-VeMAC is a multichannel MAC protocol based on VeMAC. Like VeMAC, it employs a TDMA mechanism, and supports one control channel and several service channels. For the control channel, each frame is partitioned into two disjoint sets of timeslots, which are associated with vehicles moving in opposite directions, respectively. Unlike VeMAC, which equally partitions each frame, the frame partitioning with A-VeMAC is not equal. Instead, it can adaptively vary with the vehicle traffic conditions in opposite directions. The purpose is to better support unbalanced vehicle traffic conditions in opposite conditions. With A-VeMAC, a vehicle will first make an adjustment of frame partitioning based on the current traffic conditions in opposite directions before it attempts to reserve a timeslot. After the frame partitioning adjustment is completed, it will determine a set of timeslots that are available for it to reserve and then randomly selects one for reservation from the available timeslots. For the service channels, A-VeMAC employs the same access mechanism used in VeMAC. Simulation results show that A-VeMAC can well support both balanced and unbalanced vehicle traffic conditions, and can achieve a better performance than VeMAC in terms of the channel utilization and the access collision rate, in particular, under unbalanced traffic conditions.

This paper proposes a Mobility and Activeness aware Relay Selection (MARS) algorithm for multi-hop D2D communication underlaying cellular networks. MARS takes into account the activeness and mobility of a user device in relay selection, and introduces an activeness factor and a mobility factor in calculating the utility gain of the system brought by a relay candidate. To select a relay for a pair of multi-hop D2D users, MARS first constructs a set of inactive user devices as the relay candidate set (RCS). For each relay candidate in the RCS, it then calculates the effective utility gain of the system brought by the candidate. and finally selects the one with the largest effective utility gain as the relay for the multi-hop D2D communication. Simulation results show that the proposed MARS algorithm can achieve better performance in terms of the overall system utility as compared with a random relay selection algorithm.

This paper studies the RSU deployment problem in a 2-D urban or suburban road scenario of a vehicular ad hoc network (VANET). To optimize RSU deployment, we introduce the notion of centrality in a social network to RSU deployment, and use it to measure the importance of an RSU position candidate in RSU deployment. Based on the notion of centrality, we propose a centrality-based RSU deployment approach and formulate the RSU deployment problem as a linear programming problem with the objective to maximize the total centrality of all position candidates selected for RSU deployment under the constraint of a given deployment budget. To solve the formulated problem, we analogize the problem to a 0-1 Knapsack problem and thus employ a 0-1 Knapsack algorithm to solve the problem. In the analogy, the budget in the RSU deployment problem is analogous to the bag's capacity in the Knapsack problem, the cost of deploying an RSU is analogous to an item's weight, and the centrality of a position candidate is analogous to an item's value. Simulation results show that the proposed centrality-based deployment approach can effectively improve the efficiency of the RSU deployment in terms of the coverage time ratio as compared to a random deployment approach.

We consider a control problem in wireless multi-hop networks in which source-destination pairs desire to secure communication. Specifically, a control algorithm is proposed based on the stochastic network optimization to maximize a global utility function, subject to end-to-end secrecy transmission and network stability. To achieve secure communication, we firstly exploit an independent randomization encoding strategy to guarantee the multi-hops secrecy transmission. Then, the control algorithm is decomposed into flow control, routing and resource allocation. Based on the control algorithm, each node makes decisions on the arrival confidential data as well as the users and links. The numerical analysis illustrates that the proposed algorithm can achieve a utility result, arbitrarily close to optimal value.

Network connectivity has a big impact on the performance of a vehicular ad hoc network (VANET). This paper studies the network connectivity problem in a VANET and focuses on the analysis of the downlink connectivity probability within the two-hop coverage of an RSU in a network scenario where data is broadcast from RSUs to all vehicles in the network. An analytical model is derived to calculate the two-hop downlink connectivity probability, taking into account the road condition, traffic distribution, and vehicle capability. The accuracy of the analytical model is verified through simulation results. The analytical model can be used to not only calculate the two-hop downlink connectivity probability and but also investigate the impact of different parameters on the connectivity probability, which is useful in the deployment of RSUs in a VANET.

This paper considers the BBU computing resource allocation problem in a C-RAN system. In particular, we consider the additional computing resource consumption introduced for coordination between different BBUs when the tasks of a cell is processed by different BBUs and consider load balancing among multiple BBUs in order to reduce the effect of a breakdown BBU on the system. The problem is formulated as an optimization problem with the objective to minimize the total amount of computing resources used for processing the tasks of all cells and meanwhile balance the allocation of the computing resources in all used BBUs. The formulated problem is equivalent to the classical bin-packing problem, which has proved to be NP-hard. To solve the problem, a heuristic genetic algorithm (HeuGA) is proposed to find an approximate solution to the problem. HeuGA combines a genetic algorithm (GA) with a first-fit (FF) algorithm, and consists of four steps: population initialization, fitness function construction, individual selection, and cross and mutation. Simulation results show that HeuGA outperforms the FF algorithm and the GA algorithm in terms of the number of BBUs used in the system and BBU resource allocation balancing without affecting the efficiency of the resource allocation.

This paper proposes a Cluster-based On-demand DElay tolerant routing (CODE) algorithm for VANETs. The CODE algorithm considers a higyway scenario and performs route discovery for data dissemination based on a cluster-based routing structure. To build the routing structure, CODE first configures vehicles moving on the highway into clusters and uses the relative speeds of vehicles in cluster-head election. A vehicle moving at a speed closer to the average speed of all vehicles has a higher probability to be elected as cluster head. The purpose is to maintain the stability of a cluster in order to achieve a good data delivery performance. Based on this routing structure, an on-demand delay tolerant route discovery mechanism is employed to find an available route for disseminating data packets from a source node to a destination node. Simulation results show that the proposed CODE algorithm has a better cluster stability, and can achieve a better performance in terms of end-to-end delay, packet loss ratio, and routing overhead as compared with existing LID and HD algorithms.

This paper proposes a destination and moving direction information based probabilistic routing protocol, called Advanced DIP (ADIP), for vehicular ad hoc networks (VANETs). The ADIP protocol uses different strategies for routing and data forwarding in different network scenarios. For a high-density scenario, it only considers the position of a neighbor in selecting the next hop for forwarding a data packet. For a low-density scenario, it considers not only the position of a neighbor but also the moving direction of a neighbor in selecting the next hop. The concept of an average two-hop neighbor number is introduced to identify different network scenarios. Simulation results show that in an urban street scenario ADIP does not exhibit an obvious advantage while in a highway scenario ADIP can significantly reduce the packet loss as compared with an existing routing protocol called DIP.

This paper considers the network connectivity problem in a highway scenario and builds an analytical model to analyze the steady-state connectivity probability of vehicle nodes on a unidirectional highway segment with one entrance/exit. In the analytical model, the entrance/exit is uniformly distributed on the road segment and divides the road segment into two segments. On each segment, the vehicle arrival process is a Poisson process. A vehicle node travels through the entrance/exit with a given probability. Based on such assumptions, we consider different cases according to the location distribution of the entrance/exit, and take into account the vehicle arrival rate, the vehicle speed, and the probability that a vehicle node travels through the entrance/exit in deriving the connectivity probability. The accuracy of the analytical model is verified through simulation results and the impacts of different parameters on the connectivity probability are respectively investigated through both simulation and analytical results.

This paper proposes an adaptive channel access (ACA) mechanism for LTE-U and WiFi coexistence in an unlicensed spectrum. To support the ACA mechanism, we introduce a new time-frequency structure, and a modified listen before talk (MLBT) functionality for channel contention of LTE-U users. Moreover, we also introduce a dynamic channel switch (DCS) sub-mechanism and an adaptive almost blank subframe (AABS) sub-mechanism for LTE-U users to access a channel under different levels of traffic load. The DCS sub-mechanism is intended to avoid an LTE-U user to occupy a channel for too long and thus provide more access opportunities for WiFi users when the traffic load in the network is low. The AABS sub-mechanism is intended to reserve a certain number of subframes in an LTU-U frame for use by WiFi users when the traffic load in the network is high in order to ensure fair access opportunities for WiFi users. To use the DCS and AABS sub-mechanisms, the BS in the LTE-U network and the AP in the WiFi system must report the number of their users to a mobile management entity (MME) in the core network through signaling messages, respectively. Based on the information contained in the received signaling messages, the MME will calculate the current traffic load of LTE-U and WiFi, and based on the traffic load status decide to use different sub-mechanisms in LTE-U. Simulation results show that the proposed ACA mechanism can effectively improve the system performance in terms of average user throughput.

This paper proposes an adaptive time division scheduling (ATDS) based resource allocation algorithm for device-to-device (D2D) communication underlaying cellular networks. The proposed ATDS algorithm introduces an adaptive TDS strategy, in which all D2D pairs in a cellular system are adaptively scheduled into a series of timeslots for communication based on an improved proportional fairness algorithm. For a particular timeslot, the scheduling priority of each D2D pair is first determined based on both the data transmission rate and the rate satisfaction ratio of each D2D pair. Then, a set of D2D pairs are assigned to the timeslot based on the priorities determined and only those D2D pairs with higher priorities are assigned to the timeslot. After that, the spectrum resources of cellular users are allocated for each D2D pair assigned to the timeslot. Simulation results show that the proposed ATDS algorithm can not only achieve a larger system throughput but also achieve a better fairness as compared with a couple of resource allocation algorithms that do not use time division scheduling.

This paper considers the resource allocation problem for device-to-device (D2D) communication underlaying cellular networks. In particular, we consider a system scenario where the number of D2D users is larger than that of cellular users. To address this scenario, we propose to not only allow one D2D pair to share the resources of multiple cellular users, but also allow one cellular user to share its resources with multiple D2D pairs. We formulate the resource allocation problem as a mixed integer non-linear programming (MINLP) problem with the objective to maximize the system capacity. To solve the problem, we propose a heuristic Graph-coloring resOurce ALlocation (GOAL) algorithm based on a graph-coloring approach, where the D2D pairs in the system are viewed as a set of vertexes and the resources of cellular users are viewed as a set of colors. To support resource allocation, GOAL introduces the concept of the interference negligible distance (INS) to identify those D2D pairs can simultaneously share the same spectrum resources of cellular users, and the concept of the signal to interference ratio (SIR) limited area (SLA) to identify a set of D2D pairs that cannot share the spectrum resources of a particular cellular user. Simulation results show that the proposed GOAL algorithm can significantly improve the system capacity and accommodate more D2D users.

This paper considers the resource allocation problem in device-to-device (D2D) communication underlaying cellular networks and proposes a time division scheduling (TDS) resource allocation algorithm to efficiently exploit the downlink spectrum resources of cellular users to support more D2D communication. The proposed TDS algorithm introduces a time division scheduling framework, in which the scheduling period of a base station is divided into a set of timeslots and the D2D pairs in the system are assigned to different timeslots for communication in a balanced manner in order to accommodate more D2D users in the system. In the D2D pair assignment for each timeslot, it follows a location dispersion principle in order to reduce the interference from D2D users to cellular users and thus increase the system throughput. Moreover, the minimum required data rate of each D2D pair is taken into account in the allocation of cellular resources in each timeslot. Simulation results show that the proposed TDS algorithm can significantly improve the system performance in terms of the system throughput and D2D user satisfaction ratio of a cellular system.

This paper considers the power consumption problem in a macro-cell system with a number of small cells and proposes a Distributed base station On/Off Control Mechanism (D-OCM) for reducing the power consumption of the network. Using D-OCM, an inactive small base station (SBS) can be activated by either an active SBS or the macro base station (MBS), while an active SBS can be deactivated by the active SBS itself, both in a distributed manner. To support D-OCM, each base station needs to maintain three tables to store relevant network status information on its neighbor base stations. Both the MBS and an SBS build the tables using the network status information collected from the users within their coverage. Each base station, either the MBS or an SBS, makes a decision to activate an SBS in a sleep state or to deactivate an SBS in a working state based on the traffic load within its coverage. Simulation shows that the proposed D-OCM mechanism can significantly reduce the power consumption of the network as compared a traditional control mechanism.

This paper develops an analytical model for analyzing the access performance of the ADHOC MAC protocol. A Markov chain is first constructed to describe the number of vehicles which have acquired a timeslot successfully at the end of a frame when using the ADHOC MAC protocol. Based on the Markov model, an analytical model is then derived to describe the relationship between the frame length and the channel utilization. Based on the derived analytical model, an optimal frame length that maximizes the channel utilization is further obtained. Simulation experiments are conducted to verify the effectiveness of the analytical model.

This work studies the dynamic resource allocation for device‐to‐device communication and proposes a service time prediction‐based dynamic resource allocation mechanism for a cellular system. The proposed allocation mechanism introduces the concept of ‘utility’ as a metric to reflect the quality of experience of users with different service requirements, and takes into account service time prediction in spectral resource allocation. Specifically, it enables a base station to predict the service time of a new user and the remaining service time of existing users in the system, and takes into account the predicted service time and remaining service time in calculating the effective utility gain caused by the arrival of a new user. Based on the calculated effective utility gain, the base station will select the physical resource block (PRB) with the largest effective utility gain every time it allocates a PRB to the new user. Simulation results show that the proposed dynamic allocation mechanism can significantly improve the system performance in terms of the overall average utility and the number of users successfully admitted to the system.

This paper considers the resource allocation problem for device-to-device (D2D) communication underlaying cellular networks and proposes a service-aware resource allocation (SARA) scheme for D2D communication to improve the network performance. SARA takes into account the different service requirements of D2D users. It consists of two allocation phases: on-demand resource allocation and secondary resource allocation. In the first phase, cellular user resources are allocated on demand to meet the different service requirements of D2D pairs. In the second phase, the remaining cellular user resources after the first phase are allocated to D2D users based on an objective function in order to improve the resource utilization. Simulation results show that the proposed SARA scheme can significantly improve the performance of a cellular system in terms of the satisfaction ratio of D2D pairs and the system throughput.

This paper analyzes the information delivery delay for roadside unit deployment in an intermittently connected vehicular network. An analytical model is developed to describe the relationship between the average information delivery delay and the distance between two neighbor RSUs deployed along a road. The derived model considers a straight highway scenario where two RSUs are deployed at a distance without any direct connection and vehicles are sparsely distributed on the road with road condition information randomly generated between the two neighbor RSUs. Moreover, the model takes into account the vehicle speed, the vehicle density, the likelihood of an incident, and the distance between two RSUs. The effectiveness of the derived analytical model is verified through simulation results. Given the delay requirement of some time-critical applications, this model can be used to estimate the maximum distance allowed between two neighbor RSUs, which can provide a reference basis for the deployment of RSUs in such scenarios.

This paper develops an analytical model for analyzing the access performance of the IEEE 802.11p EDCA mechanism. A 2-D Markov chain is first constructed to describe the backoff procedure of an AC queue and establish a relationship between the transmission probability and collision probability of the AC queue. Then a 1-D infinite discrete-time Markov chain is constructed to describe the contention period of an AC queue and establish another relationship between the transmission probability and collision probability of the AC queue. The two Markov models take into account the saturation condition, standard parameters, backoff counter freezing, and internal collision. Based on the two Markov chains, an analytical model is further derived to describe the relationship between the parameters of an AC queue and the access performance of the AC queue in terms of the transmission probability and the collision probability, respectively. The effectiveness of the analytical model is verified through simulation results.

Resource allocation is a critical issue in device-to-device (D2D) communication underlying cellular networks. Existing work only considers static resource allocation, which cannot well address the dynamic resource allocation problem in a real cellular network. This paper studies dynamic resource allocation for D2D communication and proposes a service time prediction based dynamic resource allocation mechanism for a cellular network system. The proposed allocation mechanism considers different service types, and introduces the concept of "utility" as a metric to reflect the performance of a user or the system, and takes into account service time prediction in spectral resource allocation. Specifically, it enables the base station to predict the service time of a new user and the remaining service time of existing users in the system, and take into account the predicted service time and remaining service time in calculating the utility gain caused by a new user. Based on the estimated utility gain, the base station will select the physical resource block (PRB) with the largest utility gain every time it allocates a PRB to the new user. Simulation results show that the proposed dynamic allocation mechanism can significantly improve the system performance in terms of the system utility and the number of users successfully admitted to the system.

This paper proposes a Capacity Oriented Resource ALlocation (CORAL) algorithm for resource allocation in device-to-device (D2D) communication underlaying mobile cellular networks. To achieve high system capacity, CORAL allows a D2D pair to share the resources of more than one cellular user. Meanwhile, it introduces the concept of a Capacity-Oriented REstricted (CORE) region for a D2D pair to determine a candidate cellular user set for the D2D pair in resource allocation, which excludes those cellular users that introduce a negative system capacity gain, and thus can help increase the system capacity and reduce the computational complexity of CORAL as well. Moreover, CORAL attempts to avoid the situation where different D2D pairs simultaneously choose the same optimal cellular user, which would otherwise cause severe inteferences between the different D2D pairs and larger interference on the optimal cellular user. In addition, CORAL can ensure that each D2D pair can be allocated the resources of at least one cellular user. Simulation results show that CORAL can achieve a good performance in terms of both the system capacity and the rate loss of all cellular users.

This paper considers distributed linear beamforming in downlink multicell multiuser orthogonal frequency-division multiple access networks. A fast convergent solution maximizing the weighted sum-rate with per base station (BS) transmiting power constraint is formulated. We approximate the noncon-vex weighted sum-rate maximization (WSRM) problem with a semidefinite relaxed solvable convex form by means of a series of approximation based on interference alignment (IA) analysis. The WSRM optimization is a two-stage optimization process. In the first stage, the IA conditions are satisfied. In the second stage, the convex approximation of the non-convex WSRM is obtained based on the consequences of IA, and high signal-to-interference-plus-noise ratio assumption. Compared to the conventional iterative distributed algorithms where the BSs exchange additional information at each iteration, the BSs of our proposed solution optimize their beamformers locally without reporting additional information during the iterative procedure.

This paper considers the cluster-head failure detection problem in WSNs and proposes a Cooperative cluster-head failure detection (CLARET) mechanism to accurately detect the failure status of a cluster head. The proposed CLARET mechanism allows the cluster head of a cluster to periodically broadcast a “heartbeat” message to all its members and each cluster member to independently detect the status of the cluster head based on the “heartbeat” messages it has received. Furthermore, it introduces a cooperative detection mechanism for a cluster member detecting a potential failure of the cluster head to confirm its detected failure by checking the status information piggybacked in the data packets sent by its neighbors. An analytical model is developed to analyze the false detection probability of CLARET. Simulation results show that the analytical model is basically effective and accurate in evaluating the false detection probability, and CLARET outperforms a traditional cluster-head failure detection mechanism in terms of both the failure detection time and the false detection probability.

This paper considers the fair access problem in vehicular ad hoc networks (VANETs) and develops an analytical model for analyzing the performance of an IEEE 802.11 DCF based fair channel access protocol in a non-saturated state. We derive the relationship between the transmission probability and the minimum contention window size of a vehicle, and the relationship between the velocity and the minimum contention window size of a vehicle in a non-saturated state. Based on the analytical model the minimum contention window size of a vehicle for a given velocity can be determined in order to achieve fair access among different vehicles. The effectiveness of the analytical model is justified through simulation results.

Broadcasting is an effective routing paradigm for data dissemination in vehicular ad hoc networks (VANETs). One concern that arises with broadcasting is the broadcast storm problem, which would cause node contentions and data collisions, and thus degrade the transmission efficiency of a network. This paper proposes a Dynamic trAnsmission delaY based broadcast (DAYcast) protocol for a VANET. To alleviate the effect of the broadcast storm and improve the transmission efficiency of the network, DAYcast only allows the effective neighbors of a source vehicle to broadcast a received data packet and the selection of the effective neighbors are based on the position information on the one-hop neighbors of the source vehicle. Meanwhile, it allows each effective neighbor to wait a certain transmission delay before it broadcasts a received packet. The transmission delay of an effective neighbor depends on the distance between the neighbor and the source vehicle, and the number of effective neighbors of the source vehicle. Simulation results show that DAYcast can effectively improve the network performance in terms of network reachability and the successful delivery ratio as compared with existing weighted p-persistence broadcasting (WPB) and slotted 1-persistence broadcasting (SPB).

This paper proposes a Destination Information based Probabilistic (DIP) routing protocol for vehicular sensor networks (VSNs). In DIP, the destination information of a packet is used to determine candidate neighbors and the moving pattern of a vehicle is used to help select the next hop for forwarding data. The design goal of this protocol is to minimize the packet loss in the network and thus improve network throughput. Simulation results show that DIP achieves a better performance than the well-known existing AODV protocol in terms of packet loss by about 20%. Moreover, DIP also has a better scalability to different packet loads than AODV.

Routing is a critical issue in vehicular ad hoc networks (VANETs). This paper considers the routing issue in both vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) communications in VANETs, and proposes a Moving dirEction and DestinAtion Location based routing (MEDAL) algorithm for supporting V2V and V2I communications. MEDAL takes advantage of both the moving directions of vehicles and the destination location to select a neighbor vehicle as the next hop for forwarding data. Unlike most existing routing algorithms, it only uses a HELLO message to obtain or update routing information without using other control messages, which largely reduces the number of control messages used in routing. Simulation results show that MEDAL can significantly improve the packet delivery ratio of the network as compared with the well-known Ad hoc On-demand Distance Vector Routing (AODV) algorithm.

ABSTRACT In this paper, we study the one‐dimensional coverage problem in a wireless sensor network (WSN) and consider a network deployed along a one‐dimensional line according to a Poisson distribution. We analyze three important parameters that are related to the problem, i.e.,expected k‐coverage proportion, full k‐coverage probability, and partial k‐coverage probability, and derive mathematical models that describe the relationships between the node density in the network and these parameters. The purpose is to calculate or estimate the node density required for achieving a given coverage probability, which is useful in the deployment of a one‐dimensional network for many applications. We first analyze the expectedk‐coverage proportion, then analyze the fullk‐coverage probability fork = 1 and the lower bound to the fullk‐coverage probability fork > 1, and finally analyze the partialk‐coverage probability fork = 1 and give a brief discussion of the partialk‐coverage probability fork > 1. The mathematical models are validated through simulation. Copyright © 2011 John Wiley & Sons, Ltd.

This paper considers the correlated data routing problem in a wireless sensor network (WSN) and proposes an entropy coding based hybrid routing algorithm to solve the problem. The proposed hybrid routing algorithm takes into account both the correlation structure of the data between different sensor nodes and the path-cost of a node, and combines an entropy coding based routing algorithm and the shortest-path-tree (SPT) routing algorithm. Specifically, for a node that is away from the sink, it employs the entropy coding based algorithm to determine the next-hop node, while for a node close to the sink it employs the SPT routing algorithm to determine the next-hop node. Simulation results show that the proposed hybrid routing algorithm can significantly improve the overall transmission cost and the total amount of data transmitted in the network as compared with the SPT routing algorithm.

This paper proposes a network coding based retransmission scheme for multicast in a mobile communication network with relay. The proposed retransmission scheme uses frequency division (FD) for resource allocation in order to achieve high transmission and retransmission efficiency in the network. Meanwhile, an efficient packet selection algorithm called Weight Pick is proposed to support the retransmission scheme. Unlike existing packet selection algorithms, Weight Pick introduces the new concept of the dynamic combination number in performing network coding. Based on this concept, a base station or a relay dynamically determines the number of packets combined or encoded in a retransmission packet based on the current packet receiving status and the combination number for each retransmission packet can be different. Simulation results show that the proposed retransmission scheme with Weight Pick can significantly improve the retransmission performance in terms of the packet loss ratio and the number of delivered packets as compared with existing network coding based retransmission schemes.

This paper considers the Slepian-Wolf coding based energy minimization clustering (SWEMC) problem in a wireless sensor network (WSN), which aims to minimize the amount of data generated within each cluster and the overall energy cost for data transmission in the network. To solve the problem, we propose a Slepian-Wolf coding based energy-efficient clustering (SWEEC) algorithm, which is based on a heuristic algorithm for solving the minimum set weight cover problem in graph theory. The proposed SWEEC algorithm considers both the correlation structure of data from different sensor nodes and the distance of a cluster head to the sink(s) in cluster head election. Using this algorithm, a sensor node with a larger data compression rate and closer to the sink has a higher probability to become a cluster head. Simulation results show that the proposed SWEEC algorithm can significantly reduce the overall energy cost for data transmission and thus improve the energy efficiency of the network as compared with an existing Slepian-Wolf coding based clustering algorithm.

This study proposes a network Coding-based RETransmission (CoRET) mechanism for supporting multicast service in mobile communication networks. CoRET introduces deterministic network coding to improve retransmission efficiency and retransmission reliability. To support CoRET, a couple of efficient packet selection algorithms are proposed, one is called ‘Most–Least’ selection and the other is called Hamming distance-based (‘Hamming-D’) selection, to select packets when performing network coding for retransmission. Most–Least selects the packets with the most retransmission requests and the least retransmission requests, whereas Hamming-D selects the packets with the largest Hamming distance. Simulation results show that with Most–Least or Hamming-D CoRET outperforms an existing retransmission scheme, ‘Random Pick’, in terms of both retransmission efficiency and retransmission reliability. To achieve the best performance of the Hamming-D algorithm, a mathematical model is further developed to analyse the impact of the number of original packets encoded into a packet for retransmission on the retransmission performance to find an optimal encoding number that can achieve the best performance.

This article proposes a destination-driven on-demand multicast routing protocol (D-ODMRP) to improve the multicast forwarding efficiency in mobile ad hoc networks (MANETs). In D-ODMRP, the path from the multicast source to a multicast destination tends to use those paths passing through another multicast destination. If such multiple paths are available, the one leading to the least extra cost is preferred. This destination-driven strategy is introduced into the on-demand construction process of a multicast forwarding structure in a popular multicast protocol ODMRP. Simulation results show that D-ODMRP can significantly improve the forwarding efficiency as compared with ODMRP. Moreover, the destination-driven strategy can also be introduced into other existing multicast routing protocols for MANETs.

This paper proposes a Markov model for analyzing the performance of periodic broadcast in vehicular ad hoc networks (VANETs). Since there is no packet retransmission in broadcast, we only consider a one-dimensional Markov model. In the proposed model, we assume that the traffic condition is unsaturated and introduce a state to represent the situation when there is no packet waiting for transmission in the buffer of a node. Meanwhile, the state of a backoff counter changes only when the channel is sensed idle. When the channel is busy, the state of the backoff counter does not change. Moreover, we use a discrete time D/M/1 queue to model the periodic broadcast of each node. Based on this model, we also show numerical results to analyze the performance of the periodic broadcast in VANETs in terms of packet collision probability and average packet delay.

In this paper, we consider the double-blindness problem in large-scale wireless sensor networks (WSNs) with mobile sinks, where the mobile sink(s) and data do not know the locations of each other a priori. We first propose a random line walk mechanism for message forwarding and based on this forwarding mechanism we further propose an efficient data discovery scheme called Double Cross to address the double-blindness problem. Double Cross exploits a simple geometric property of a planar, i.e., for a couple of pairs of orthogonal lines in a planar, the probability that they intersect within the planar is larger than 99%. However, it does not depend on the geographic location or directional information of a node, which is difficult to obtain in such networks. Instead, each sensor only needs to know the distances between neighbor nodes within its transmission range. Analytical and simulation results show that Double Cross can achieve a high successful discovery rate with low energy consumption.

This paper proposes a network Coding based RETransmission (CoRET) scheme for supporting multicast service in mobile communication networks. CoRET introduces network coding and employs a Hamming distance based packet selection algorithm to select packets for encoding in order to improve the retransmission performance in terms of efficiency and reliability. Simulation results show that CoRET can achieve better retransmission performance than an existing retransmission scheme, Random Pick. Moreover, a mathematical model is developed to analyze the impact of the number of original packets encoded into a packet for retransmission (i.e., encoding number) on the retransmission performance in order to find an optimal encoding number that can achieve the best performance. Numerical results show that for a reliable transmission channel a smaller encoding number can achieve a better performance while for an unreliable channel a larger encoding number is preferred.

In wireless sensor networks (WSNs), a sensor node may not always be able to report all its readings to the sink node because of limited network resources. Thus, it is desirable to have an efficient data reporting strategy with high accuracy for data reporting in such networks. In this study, the authors study data reporting in a WSN with no a priori information on future sensor readings and analyse the rationale behind the widely used equi-interval data reporting strategy in terms of the accuracy in data collection. To support the equi-interval data reporting strategy, the authors propose an adaptive rate control algorithm to achieve the maximum data reporting rate under the bandwidth constraint, and further extend this algorithm to one that can adaptively adjust the reporting rate of a sensor based on the residual energy of the sensor in order to prolong the network lifetime. Simulation results show that the equi-interval data reporting strategy can achieve higher accuracy than other strategies and with the proposed rate control algorithms it can further improve the network performance in terms of data accuracy and network lifetime.

The authors propose a new geographic hole-bypassing forwarding (HBF) protocol to address the hole diffusion problem in wireless sensor networks (WSNs). To support efficient hole-bypassing, the HBF protocol models a hole using a virtual circle whose radius is adjustable within a certain range and is calculated on a per-packet basis. The information associated with the virtual circle will be used, if needed, for selecting an anchor point to bypass the hole in order for a packet to reach a particular sink node. The design objective of the HBF protocol is to balance the traffic load among the nodes near an actual hole boundary. Using the HBF protocol, a packet is always sent to the closest sink and the extra distance for hole-bypassing is considered in the delivery of data packets to reach the sinks (or some of them) in the network. The simulation results show that HBF outperforms existing hole-bypassing protocols in terms of packet delivery ratio and network lifetime.

Abstract Node positioning is a useful service for a vehicular communication system. The global positioning system (GPS) is the most popular positioning technique widely used in vehicular communication systems. However, GPS cannot work effectively in many situations, for example, in a tunnel or under a bridge. For accurate positioning in such situations, it is desirable to have more reliable positioning techniques. In this paper, we propose a novel method for node positioning in vehicular communication systems. Unlike existing positioning methods that employ a roadside node as an observation station, the proposed positioning method allows a vehicular node to play the role of an observation station, and estimates the vehicular node's absolute coordinates based on the measurement of the Doppler frequency shift, the velocity of the vehicular node, and the known absolute coordinates of roadside nodes. To improve positioning accuracy, it introduces extended Kalman filtering (EKF) to mitigate the effects of channel noise and multipath interference. Analytical and simulation results show that the proposed positioning method can significantly improve the positioning accuracy compared with differential GPS and reduce the positioning delay. Copyright © 2009 John Wiley & Sons, Ltd.

This paper investigates asymmetric two-way relay channels (TWRCs), where a couple of source nodes exchange data with different flow rates via a relay node. A novel mapping codebook-based physical network coding scheme is proposed for improving the performance of such channels. The proposed network coding scheme introduces a mapping codebook that contains several subcodebooks and adaptively selects a subcodebook from the mapping codebook in each data exchange based on the information of signal phase difference. Moreover, distributed transmission power control is also introduced to simplify codebook design and facilitate physical network coding. Simulation results show that it can significantly improve the BER performance as compared to analog network coding. It is also robust to the phase estimation error and the power control error, in particular, in a lower SNR region.

Abstract This paper proposes a joint data aggregation and encryption scheme using Slepian‐Wolf coding for efficient and secured data transmission in clustered wireless sensor networks (WSNs). We first consider the optimal intra‐cluster rate allocation problem in using Slepian‐Wolf coding for data aggregation, which aims at finding a rate allocation subject to Slepian‐Wolf theorem such that the total energy consumed by all sensor nodes in a cluster for sending encoded data is minimized. Based on the properties of Slepian‐Wolf coding with optimal intra‐cluster rate allocation, a novel encryption mechanism, called spatially selective encryption, is then proposed for data encryption within a single cluster. This encryption mechanism only requires a cluster head to encrypt its data while allowing all its cluster members to send their data without performing any encryption. In this way, the data from all cluster members can be protected as long as the data of the cluster head (calledvirtual key) is protected. This can significantly reduce the energy consumption for performing data encryption. Furthermore, an energy‐efficient key establishment protocol is also proposed to securely and efficiently establish the key used for encrypting the data of a cluster head. Simulation results show that the joint data aggregation and encryption scheme can significantly improve energy efficiency in data transmission while providing a high level of data security. Copyright © 2009 John Wiley & Sons, Ltd.

Abstract In wireless sensor networks (WSNs), a mobile sink can help eliminate the hotspot effect in the vicinity of the sink, which can balance the traffic load in the network and thus improve the network performance. Location‐based routing is an effective routing paradigm for supporting sink mobility in WSNs with mobile sinks (mWSNs). To support efficient location‐based routing, scalable location service must be provided to advertise the location information of mobile sinks in an mWSN. In this paper, we propose a new hierarchical location service for supporting location‐based routing in mWSNs. The proposed location service divides an mWSN into a grid structure and exploits the characteristics of static sensors and mobile sinks in selecting location servers. It can build, maintain, and update the grid‐spaced network structureviaa simple hashing function. To reduce the location update cost, a hierarchy structure is built by choosing a subset of location servers in the network to store the location information of mobile sinks. The simulation results show that the proposed location service can significantly reduce the communication overhead caused by sink mobility while maintaining high routing performance, and scales well in terms of network size and sink number. Copyright © 2009 John Wiley & Sons, Ltd.

In this paper, we propose a novel medium access control (MAC) protocol called flow-driven MAC (FD-MAC) for mobile ad hoc networks (MANETs). FD-MAC is a slotted MAC protocol that combines random access and reservation-based access, and introduces the concept of "data flow" and a flow-driven mechanism for resource reservation. For short-duration data flows, it employs a random access protocol, while for long-duration data flows it employs a flow-driven reservation-based access protocol. The purpose is to reduce data collision and thus improve channel utilization so that the network performance in terms of throughput is improved. Simulation results show that FD-MAC can significantly improve the performance in terms of throughput compared with the MACA and Slotted ALOHA protocols, and performs better than IEEE 802.11 in terms of throughput and latency under medium and high traffic load.

This special issue includes a collection of 19 outstanding research papers which cover a diversity of topics on the application of network coding in wireless communication networks.

Abstract This paper proposes a cooperative fault‐detection mechanism for detecting cluster‐head failures in cluster‐based UnderWater Sensor Networks (UWSNs). The proposed detection mechanism aims to accurately and fast detect the failure of a cluster head in order to avoid unnecessary energy consumption caused by a mistaken detection. For this purpose, it allows each cluster member to independently detect the fault status of its cluster head and then employs a distributed agreement protocol to reach an agreement on the fault status of the cluster head among multiple cluster members. It runs concurrently with normal network operation by periodically performing a detection process at each cluster member. To reduce energy consumption, it uses a time division multiple access medium access control (TDMA MAC) protocol and makes use of the data periodically sent by a cluster head as the heartbeats for fault detection. A couple of forward and backward time‐division‐multiplexing (TDM) frames are specially structured for enabling multiple cluster members to reach an agreement within two frames in each detection process. Moreover, a schedule generation algorithm is also proposed for a cluster head to generate the transmission schedule in the forward and backward frames. Through simulation results, we show that the proposed detection mechanism can achieve high detection accuracy under high packet loss rates in the harsh underwater environment, and can detect a cluster‐head failure faster than a traditional fault‐detection mechanism within a delay bound of two TDM frames. Copyright © 2008 John Wiley & Sons, Ltd.

Fault localization is an important issue in all-optical networks. The Limited Perimeter Vector Matching (LVM) protocol is a novel fault localization protocol for localizing single-link failures in all-optical networks. In this paper, we study the fault localization optimization problem in applying the LVM protocol to static networks, where traffic (or lightpath) demand is known a priori. Given the traffic demand, the fault localization optimization problem is to optimize the traffic distribution so that the fault localization probability in terms of the number of localized links can be maximized. We formulate the problem into an integer linear programming problem and use CPLEX to solve the problem. We show through numerical results that by optimizing the traffic distribution the fault localization probability in terms of the number of localized links can be maximized. Moreover, the solution to the problem can also provide the maximum number of wavelengths needed on each link to obtain the maximum fault localization probability.

Sink's vicinity is a hotspot area in a wireless sensor network, and has a significant impact on the normal operation and network performance of the entire network. A hybrid MAC protocol makes use of the advantages of both CSMA/CA and TDMA protocols and adaptively switches between the two protocols based on dynamic traffic load, and can thus improve the network performance within the sink's vicinity. In this paper, we study MAC for the sink's vicinity in a WSN. We develop performance models for CSMA/CA and TDMA systems and compare their performance under both non-saturation and saturation conditions based on the developed performance models. Through simulation results, we verify that the accuracy and correctness of the performance models. Moreover, the analytical results based on the performance models can be used as a guide for determining the optimal load point for protocol switch and designing an optimal hybrid MAC protocol for the sink's vicinity.

Node clustering has been widely considered in underwater sensor networks (UWSNs) to improve energy efficiency and prolong network lifetime. Network survivability is a great concern in cluster-based UWSNs. In this paper, we propose a dependable clustering protocol to provide a survivable cluster hierarchy against cluster-head failures in such networks. The proposed clustering protocol attempts to select a primary cluster head and a backup cluster head during clustering so that the cluster members associated with the failed cluster head can quickly switch over to the backup cluster head in the event of a cluster-head failure. Meanwhile, it attempts to select a set of clusters with minimum total cost so that network lifetime can be prolonged to ensure long-term underwater environmental monitoring. Simulation results show that the protocol can effectively enhance network survivability and improve network capacity in the event of cluster-head failures.

In this paper, we propose an efficient fault-prevention clustering protocol for improving the lifetime and robustness of underwater sensor networks (UWSNs). The proposed clustering protocol takes into account both the reliability and residual energy status of each sensor node during clustering, and attempts to select those healthy nodes as cluster heads through failure prediction, cost evaluation, and clustering optimization. The purpose of failure prediction is to predict the potential failure of an underwater sensor based on its lifetime distribution so that those unhealthy nodes are prevented from being selected as cluster heads. Cost evaluation is introduced to evaluate the cost caused by the failure of a cluster head. Clustering optimization aims to construct a cluster hierarchy that minimizes the overall cost of all selected clusters based on the cost evaluation of each sensor node. The simulation results show that the proposed clustering protocol can not only significantly prolong network lifetime, but also improves network robustness and capacity compared with existing clustering protocols.

In this paper, we propose a fault localization protocol for localizing single-link failures in multi-domain all-optical networks. This protocol is based on a limited-perimeter vector matching (LVM) mechanism, which restricts fault localization in a smaller perimeter area and can thus significantly reduce the time and space complexities of fault localization. By assuming power monitoring available only at edge nodes, this protocol can localize both inter-domain and intra-domain link failures without exchanging any internal confidential domain-specific information (e.g., topology and traffic information) between different domains. We show through analytical results that it can not only fast localize an inter-domain link failure between different domains but also localize an intra-domain link failure that affects inter-domain traffic faster than the open-shortest-path-first (OSPF) protocol in a large network.

The ocean, which covers about two-third of the Earth surface, is a largely unexplored world that has fascinated humans since the beginning of human history. Over a long period of time, there is a great interest in exploring the ocean and other underwater environments (e.g., rivers, lakes, and reservoirs) for scientific, environmental, commercial, and military purposes. With the increasing demand for acquiring localized, precise and real-time knowledge of the harsh underwater environments, traditional underwater exploration technologies such as SONAR or other remote sensing technologies can no longer meet such demands. Underwater sensor networks are an emerging network paradigm which provides a promising solution to exploring the ocean and underwater environments. An underwater sensor network consists of a number of underwater sensor nodes with sensing, data processing, and communication capabilities, which are deployed in a region of interest and collaborate to accomplish a common task such as underwater environmental monitoring, mine reconnaissance, and military surveillance. Driven by a broad range of potential applications in both civilian and military areas as well as rapid technological advances in microelectronics, wireless communications, and embedded processing, underwater sensor networks have recently received much attention from both academia and industry. Distinct from terrestrial sensor networks, an underwater sensor network has some unique characteristics that need to be particularly addressed such as low communication bandwidth, large propagation delay, harsh geographical environment, and floating node mobility. These unique characteristics present many challenges in the design of underwater sensor networks, which have recently motivated a growing interest and a considerable amount of research activities in this emerging area. This special issue includes a collection of eight outstanding research papers, which cover a diversity of topics on the design of network architectures and protocols for underwater sensor networks. The issue begins with an invited paper, ‘Prospects and Problems of Wireless Communication for Underwater Sensor Networks,’ contributed by Jun-Hong Cui et al. This paper reviews the physical fundamentals and engineering implementations for efficient information exchange via wireless communications using physical waves as the carrier among nodes in an underwater sensor network. It also makes recommendations for the selection of the communication carrier for underwater sensor networks with engineering countermeasures that can possibly enhance the communication efficiency in specified underwater environments. In the second paper, ‘Coverage and Connectivity in Three-Dimensional Underwater Sensor Networks,’ Alam and Haas studied the node deployment problem in a 3D underwater sensor network and provided a solution to the coverage and connectivity problem with limited and full communication redundancy requirements. In the third paper, ‘Placement of Multiple Mobile Data Collectors in Underwater Acoustic Sensor Networks,’ Alsalih et al. studied the placement problem of mobile data collectors in underwater sensor networks and proposed two routing and placement schemes. One is delay-tolerant placement and routing (DTPR), which can maximize the network lifetime without any delay consideration. The other is delay-constrained placement and routing (DCPR), which can maximize the network lifetime with an upper bound on the maximum delay. The fourth paper, ‘Target Tracking Based on a Distributed Particle Filter in Underwater Sensor Networks,’ by Huang et al. proposes two algorithms for tracking mobile targets in cluster-based underwater sensor networks based on a distributed particle filter. One of them can achieve higher tracking accuracy while the other can significantly reduce the communication cost, energy cost, and tracking response time. In the fifth paper, ‘Utilizing Acoustic Propagation Delay to Design MAC Protocols for Underwater Wireless Sensor Networks,’ Guo et al. proposed an efficient MAC protocol for underwater sensor networks, which makes use of the propagation delay to avoid collisions, thus reducing control overhead and energy consumption. In the sixth paper, ‘Path Unaware Layered Routing Protocol (PULRP) With Non-Uniform Node Distribution for Underwater Sensor Networks,’ Gopi et al. proposed a PULRP for 2D underwater sensor networks with mobile nodes, which has been demonstrated to have better throughput and delay performance as compared to the underwater diffusion (UWD) algorithm. In the seventh paper, ‘PAS: Probability and Sub-Optimal Distance (SOD)-Based Lifetime Prolonging Strategy for Underwater Acoustic Sensor Networks,’ Dou et al. proposed a couple of lifetime prolonging strategies for underwater sensor networks: probability-based energy-balancing (PEB) strategy and SOD-based data transmission strategy. They showed through simulation results that both strategies can efficiently save energy consumption and thus prolong the network lifetime. In the last paper, ‘Development of Routing Protocols for the Solar-Powered Autonomous Underwater Vehicle (SAUV) Platform,’ Bartos et al. presented a summary of the experience obtained in the development, evaluation, and field testing of two routing protocols for the SAUV platform. Useful suggestions based on field experience are also presented for improving the design and evaluation of routing protocols for a harsh underwater environment. We thank all the authors who submitted their papers to this special issue. Owing to the limitation of space, we can include only eight papers in the issue. We are grateful to all the reviewers for their time and efforts in carefully reviewing all the papers and providing valuable review comments. We also thank the Editor-in-Chief, Mohsen Guizani, for his continuous support for this special issue, and all the publication staff for their support during the publishing process. It is our hope that the papers included in this special issue present a good snapshot of the latest research progress in the design of network architectures and protocols for underwater sensor networks and become an important reference for researchers and practitioners in the area. Finally, we hope that the readers will find this special issue timely and informative.

In this paper, we propose a combined data aggregation and encryption scheme using Slepain-Wolf coding for efficient and secured data transmission in wireless sensor networks (WSNs). We first study the optimal intra-cluster rate allocation problem in using Slepain-Wolf coding for data aggregation, which aims to find a rate allocation subject to Slepian-Wolf theorem such that the total energy consumed by all sensor nodes in the cluster for sending encoded data is minimized. Based on the properties of Slepain-Wolf coding with optimal intra-cluster rate allocation, we then propose a novel encryption mechanism, called spatially selective encryption, for data encryption within a single cluster. This encryption mechanism only requires the cluster head to encrypt its data while allowing all cluster members to send their data without performing any encryption. Using this mechanism, as long as the data of the cluster head (or thevirtualkey) is protected, the data from all cluster members can also be protected, which can significantly reduce the energy consumption for data encryption. Furthermore, an energy-efficient key establishment protocol is also proposed to securely and efficiently establish the key used for encrypting thevisualkey.

In this paper, we study the application of distributed lossy source coding for data aggregation in cluster-based wireless sensor networks (WSNs). We consider a clustered lossy coding (CLC) problem, which aims to select a set of disjoint clusters to cover the whole network such that the total rate of encoded data generated by all clusters or nodes in the network is minimized, given the spatial correlation structure of the network and a couple of total and individual distortion constraints. To solve this problem, we first prove that the overall optimization problem can be decoupled into two independent optimization problems: an optimal clustering problem and an optimal distortion allocation problem. The first problem aims at constructing a clustered hierarchy to minimize the global network entropy without considering distortion allocation, while the second problem aims to optimally allocate a distortion to each sensor node under the given distortion constraints without considering node clustering. We then present a distributed optimal-compression clustering protocol to solve the first problem and use Lagrange multipliers to solve the second problem.

In this paper, we propose an agreement-based fault detection mechanism for detecting cluster-head failures in clustered UnderWater Sensor Networks (UWSNs). The proposed detection mechanism aims to accurately detect the failure of a cluster head in order to avoid unnecessary energy consumption caused by a mistaken detection. For this purpose, it allows each cluster member to independently detect the fault status of its cluster head and at the same time employs a distributed agreement protocol to reach an agreement on the fault status of the cluster head among multiple cluster members. The detection mechanism is based a TDMA MAC protocol used in the network and runs concurrently with normal network operation by periodically performing a distributed detection process at each cluster member. To reduce energy consumption, it makes use of the data periodically sent by a cluster head as the heartbeats for fault detection. A couple of forward and backward TDM frames are specially structured for enabling multiple cluster members to reach an agreement within two frames in each detection process. Moreover, a schedule generation algorithm is also proposed for a cluster head to generate the transmission schedule of the forward and backward frames. Through simulation results, we show that the proposed detection mechanism can achieve high detection accuracy under high packet loss rates in the harsh underwater environment, and can faster detect a cluster-head failure than a traditional fault detection mechanism.

In this paper, we study the node clustering problem in underwater sensor networks (UWSNs). We formulate the problem into a cluster-centric cost-based optimization problem with an objective to improve the energy efficiency and prolong the lifetime of the network. For this purpose, a cost metric is defined for a potential cluster, which takes into account three important parameters that are relevant to the energy status of the cluster, including (1) the total energy consumption of the cluster members for sending data to the cluster head; (2) the residual energy of the cluster head and its cluster members; and (3) the relative location between the cluster head and the underwater sink (uw-sink). To solve the formulated problem, a novel distributed clustering protocol called minimum-cost clustering protocol (MCCP) is proposed, which selects a set of non-overlapping clusters from all potential clusters based on the cost metric assigned to each potential cluster and attempts to minimize the overall cost of the selected clusters. MCCP can adapt geographical cluster head distribution to the traffic pattern in the network and thus avoid the formation of hot spots around the uw-sink. It can also balance the traffic load between cluster heads and cluster members through periodical re-clustering the sensor nodes in the network. Simulation results show that MCCP significantly improves the energy efficiency and the lifetime of a UWSN as compared with the well-known HEED protocol.

Slepian-Wolf coding is a promising distributed source coding technique that can completely remove the data redundancy caused by the spatially correlated observations in wireless sensor networks (WSNs). In this paper, we study the major problems in applying Slepian-Wolf coding for data aggregation in cluster-based WSNs with an objective to optimize data compression so that the total amount of data in the whole network is minimized. We first consider the clustered Slepian-Wolf coding problem, which aims at selecting a set of disjoint potential clusters to cover the whole network such that the global compression gain of Slepian-Wolf coding is maximized. To solve this problem, a distributed optimal-compression clustering protocol (DOC2) is proposed. Under the optimal cluster hierarchy constructed by DOC2, we then consider the optimal intra-cluster rate allocation problem and present an approximation algorithm that can find an optimal rate allocation within each cluster to minimize the intra-cluster communication cost. With the optimal intra-cluster rate allocation found, the procedures to perform Slepian-Wolf coding within a cluster are also presented.

In this paper, we study the issue of dynamically selecting shortest paths in wavelength-routed networks. We present several fast shortest path selection algorithms for networks with and without wavelength conversions. The presented algorithms employ the strategies of sequential search, backward routing, and informed search. Simulation results demonstrate that our presented algorithms can significantly reduce the average-case running time in identifying shortest paths in wavelength-routed networks.

Medium access control (MAC) is one of the most crucial issues in Ethernet passive optical networks (EPONs). To prevent data of different optical network units (ONUs) from collision in the upstream direction, an EPON system must employ a MAC mechanism to arbitrate access to the shared upstream channel and at the same time efficiently share the bandwidth of the upstream channel among all ONUs. In this paper, we present an adaptive MAC polling protocol for an EPON system. This polling protocol uses an adaptive scheduling algorithm called the earliest-packet-first (EPF) algorithm that schedules the transmission order of different ONUs based on the arrival time of the first packet waiting in the queue of each ONU and always schedules the ONU with the earliest packet to transmit first in each polling. The purpose is to reduce the packet delay in the system and thus provide better quality of service for end users.

This paper proposes a multi-initiation mechanism for dynamic path restoration to handle single-link failures in GMPLS-based WDM networks. This mechanism allows multiple network nodes on the primary path of a disrupted connection to participate in the restoration of the disrupted connection. Each of the nodes respectively initiates a restoration process upon the detection or notification of a link failure. In each of the processes, the initiating node attempts to dynamically establish a backup path for the disrupted connection. The destination node acts as a coordinator among multiple restoration processes. The purpose is to reduce the path restoration time so that a backup path can be provisioned more quickly for each disrupted connection that traverses a failed link. Based on this mechanism, a path restoration protocol is then presented and the performance of the protocol is evaluated through simulation experiments in terms of path restoration time and path restoration probability.

This paper studies the virtual topology reconfiguration problem for all-optical wavelength-routed WDM networks. We consider dynamic reconfiguration in response to changing traffic demands and formulate the problem as an Integer Programming problem with multiple optimization objectives, aiming at maximizing the network throughput, minimizing the average propagation delay over a lightpath, and minimizing the reconfiguration cost. We use heuristics to obtain approximate optimal solutions and propose a balanced alternate routing algorithm (BARA) based on a genetic algorithm. To make the problem computational tractable, we assume wavelength converters at each network node and approximately divide the optimization process into two relatively independent stages: route computation and lightpath routing. At the route computing stage, the aim is to compute a set of alternate routes for each pair of source and destination nodes in the network. At the lightpath routing stage, the aim is to decide an "optimal" route for each of the lightpaths between a pair of source and destination nodes from a set of alternate routes, and the decisions are subject to the formulated constraints and optimization objectives. Through simulation experiments, we show the effectiveness of BARA.

Advance reservation is a useful network service that can not only provide guaranteed services for real-time applications but also allow networks to better plan their resource allocations. In this paper, we explore advance reservation in optical networks and focus on the design of effective routing and wavelength assignment (RWA) algorithms to support advance reservation service in wavelength-routed WDM networks. We first introduce the advance reservation concept and present an advance reservation model. Based on this reservation model, we study the design of effective RWA algorithms for different types of advance reservation and give some example algorithms. We also discuss related issues involved in supporting the coexistence of advance and immediate reservations. In addition, we briefly review some related work on advance reservation and the underlying RWA problem in wavelength-routed WDM networks.

This paper studies the virtual topology design and reconfiguration problem of virtual private networks (VPN) over all-optical WDM networks. We formulate the problem as an integer programming problem with objectives to maximize the network throughput, and to minimize the average end-to-end propagation delay, and to minimize the reconfiguration cost. Load balancing and alternate routing are considered in order to increase the network throughput. A balanced alternate routing algorithm (BARA) is also proposed to obtain approximate optimal solutions to the formulated problem. To make the problem computationally tractable, we assume wavelength converters at network nodes and approximately divide the optimization process into two relatively independent stages: route computing and lightpath routing. To improve the computational efficiency, a genetic algorithm is used in BARA. The effectiveness of BARA is shown through simulation results.

This paper studies distributed lightpath control in wavelength-routed WDM networks. We propose to introduce a destination-routing mechanism for lightpath establishment, which uses global network state information for wavelength routing and allows the destination node of a lightpath to decide the route. The purpose is to use the most recent network state information to make a routing decision so that wavelength reservation failures can be minimized. Meanwhile, we also consider connection restoration in the occurrence of a link failure and introduce a destination-initiating mechanism that allows the destination node of a broken connection to initiate a connection restoration process. The purpose is to reduce the connection restoration time so that a backup path can be provisioned rapidly. Based on both mechanisms, we further propose an efficient distributed control protocol for lightpath establishment and restoration. The paper explicitly describes the major procedures of the protocol, discusses its significant properties, and evaluates its performance in terms of the request blocking probability, and the connection setup or restoration time.

Advance reservation is a useful network service that can not only provide guaranteed services for network users but also allow networks to better plan their resource allocations. This paper studies advance reservation for wavelength-routed WDM networks. The focus is primarily on effective routing and wavelength assignment (RWA) solutions to supporting different types of advance reservations as well as the coexistence of both advance and immediate reservations. The paper briefly reviews related work in advance reservation, presents the potential advance reservation problems, and defines an advance reservation model for wavelength-routed WDM networks. Based on this reservation model, we develop effective RWA solutions to supporting different types of advance reservations. We also propose and discuss effective control mechanisms for supporting the coexistence of both advance and immediate reservations.