In this paper, we study a UAV-Parking-Vehicles assisted Mobile Edge Computing (MEC) network for providing enhanced edge computing services, where a UAV serves to relay ground users’ tasks to parking vehicles having idle computing resources in the vicinity for processing.We formulate the problem of average task delay minimization in this case as a long-term discrete mixed integer programming problem. We transform this problem into two subproblems: Service deployment optimization problem at large time scale and task offloading optimization problem at small time scale. For the former, we define a system utility for measuring the effect of a service deployment profile at parking vehicles, formulate the system utility minimization problem for optimizing the service deployment at vehicles, and propose a Computing resource allocation and Genetic Algorithm based Service deployment Algorithm (CGSA) for obtaining optimized service deployment profiles at parking vehicles on a per cycle basis. For the latter, we propose a Bandwidth allocation, Task offloading, and Transmission scheduling Algorithm (BTTA) for determining optimized task offloading profiles for all users on a per time slot basis. Extensive simulation results show the high performance of our proposed algorithms compared with baseline algorithms.

Unmanned Aerial Vehicles (UAVs) enabled Mobile Edge Computing (MEC) has been an attractive paradigm for providing flexible and high-quality offloading services to ground users. In this paper, we study a hierarchical aerial MEC network architecture for improved quality of user experiences. Specifically, we study a bilevel UAV-enabled MEC network where a fixed-wing UAV (F-UAV) and multiple rotor UAVs (R-UAVs) are jointly deployed to provide continuous MEC services to ground users with dynamic demands. We formulate a long-term optimization problem for minimizing the utility of all users while considering the stability of task queue backlogs and energy consumption budgets at users and R-UAVs, where user utility measures the per-slot task processing performance at user side. We apply Lyapunov optimization technique to decompose the original problem into deterministic per-slot optimization subproblems. We derive the optimal offloading conditions at different types of edge nodes. We accordingly propose a Bilevel UAVs based Online Task processing and Resource allocation Algorithm (BOTRA) for determining the offloading and local processing profiles at users. Extensive simulation experiment results show the high performance of the proposed BOTRA algorithm compared with benchmark algorithms.

Task assignment is a key issue in mobile crowdsensing (MCS). Most existing work in this area has focused on the task assignment for the single platform scenario, which can cause considerable waste of limited human resources or reduced task completion rate due to potential spatial mismatching between distributions of users and tasks. In this article, we study multiplatform cooperative task assignment. The design goal is to maximize the social welfare while ensuring cooperative and individual rationality. We formulate this problem, transform it to a maximum value flow problem, and prove its NP-hardness. We first propose a greedy-maximum-flow-based task matching (GMTA) mechanism for interplatform task matching. In GMTA, there are two phases in each time slot: 1) in the former phase, earliest-deadline-first-based intraplatform optimal task assignment is carried out at each individual platform and 2) in the second phase, greedy-maximum-flow-based task matching is carried out to perform interplatform cooperative task assignment for those overloaded tasks in the first phase. We then enhance GMTA by designing an iterative-maximum-flow-based task matching (IMTA) mechanism, which is to achieve enhanced social welfare at the cost of increased computational overhead. We deduce time complexities of both mechanisms, and prove that they satisfy cooperative and individual rationality. Extensive simulations are conducted and the simulation results demonstrate the effectiveness of our proposed mechanisms.

Mobile crowdsensing (MCS) is a cost-effective sensing paradigm by incentivizing mobile users to perform sensing tasks using their smartphones with rich embedded sensors. An important problem in MCS is how to achieve high task completion rate for location dependent tasks since some of them can be far away from potential users. Most existing work in this aspect assumes that there is only one service platform without consideration of existence of multiple platforms and also impact of their cooperation on task completion rate. In this paper, we design a task bundling based multi-platform cooperation mechanism (TBMCM) for completion of location dependent sensing tasks. The design objective is to maximize the system profit while improving the task completion rate. TBMCM works in a slot-by-slot manner. In each slot, each platform first assigns its tasks and task bundles to its registered users through reverse auctions and then submits information about its idle users and not-assigned-yet unpopular tasks to a cross-platform cooperation managing entity (CCM) for cross-platform cooperation. Then, the CCM entity releases the tasks and task bundles created using its collected tasks to the idle users. Meanwhile, each platform has the option to bundle its own tasks with the tasks provided by the CCM entity. A task bundling method is then designed to perform effective bundling between unpopular and popular tasks for improved task completion rate. We show the proposed mechanism satisfies individual and cooperative rationality. Extensive simulation results show the high performance of TBMCM in terms of task completion rate and system profit.

Scalable consensus protocol is essential for providing high-throughput and secure blockchain services in wireless networks. In this article, we propose a scalable credible-committee-based blockchain consensus (SCBC) protocol for resource-limited multihop wireless networks, which contains the following key designs: 1) credit-based committee selection algorithm, which improves the system security by selecting credible committee members; 2) scalable credible-committee-based consensus algorithm, which supports efficient consensuses using small-sized committee and threshold signatures; and 3) criticality-based localized broadcast algorithm, which is designed to suppress broadcast redundancy and further improves the consensus efficiency. Thorough security analyses show that SCBC satisfies both safety and liveness properties, and can resist more attacks than traditional Byzantine fault-tolerant consensus protocols. We deduce the message complexity of SCBC. Extensive simulation results demonstrate that our proposed protocol SCBC outperforms existing work in terms of throughput and consensus latency.

Opportunistic Mobile Crowdsensing (MCS) is an attractive and cost-effective sensing paradigm because it does not affect workers' daily routines. However, its opportunistic nature in task executions can lead to low task completion rate for deadline-sensitive tasks as compared with participatory sensing. Besides, existing work in opportunistic M CS lacks of study on how to effectively coordinate among multiple service platforms for idle worker sharing so as to improve the sensing performance. In this paper, we design a multi-platform cooperation based incentive mechanism (MPCIM) for deadline-sensitive task assignment in the context of opportunistic mobile crowdsensing. The design objective is to maximize the system profit while improving the task completion rate. In MPCIM, each platform first decides how many idle workers it can provide and also how many tasks it needs assistance at different locations; Then, a cross-platform managing entity is responsible for performing maximal matching between the idle workers and excessive tasks among different platforms to improve the task completion rate while respecting individual rationality and cooperative rationality. Extensive simulation results show that MPCIM can effectively improve the system profit and also task completion rate.

Mobile crowdsensing has become a promising sensing paradigm with the popularization of mobile devices. In this paper, we focus on an opportunistic mobile crowdsensing scenario where there are multiple task requesters and users, who move in an opportunistic way in the target environment. When a task requester encounters a user, he can assign some of his held tasks to the user and receive corresponding task results when they re-encounter sometime later. In this paper, we study how to minimize the largest makespan of all requesters for the task result collections. To address this issue, we propose a cluster based largest makespan sensitive online task assignment (C-LOTA) algorithm. C-LOTA first performs two-phase clustering which clusters the users into different clusters, one for each task requester, based on their relativeness to the task requesters and also the task workloads at different requesters. C-LOTA then iteratively performs greedy intra-cluster task assignment such that largest task is firstly assigned and the first idle user always takes the task, until all tasks are assigned. We present the detailed algorithm design of C-LOTA. We deduce its computation complexity. Simulation results show that C-LOTA can achieve much better performance compared with existing work.

Peer review refers to the process of a group of reviewers jointly assessing a thesis or a journal article. Reviewer assignment constitutes the major challenge in this process. However, many studies only focus on the predictive accuracy of the matching degree between reviewers and theses. Recent works show that the unitary accuracy metric can not satisfy the requirement of maximizing the comprehensive performance of the reviewer recommendation system. In this paper, we introduce a Chinese thesis reviewer personalized recommendation model(TRPRM) for the reviewer assignment problem. The TRPRM focuses on an extra crucial metric in recommendation system evaluation: novelty. The model uses a feature crossing method to mine the interactions among different research interests of author. Moreover, we analyze the role of accuracy and novelty as indicators of recommendation quality and present novel ways of how to measure them. Lastly, we apply our model to the reviewer assignment problem using a real dataset from a university in China to verify the comprehensive performance of our proposed approach.

Mobile video traffic has experienced explosive growth in recent years due to the rapid development of mobile intelligent terminals and cellular communication technologies. The rapid growth of mobile video traffic has brought significant energy expenditure for mobile network operators. To reduce the energy expenditure, one promising solution is to exploit renewable energy harvested from surrounding environments for cellular traffic delivery. In this article, we investigate mobile video streaming in green cellular networks with hybrid energy sources, i.e., grid energy and ambient energy, to optimize both video quality and energy expenditure. Specifically, we formulate a stochastic optimization problem to maximize the long-term time-averaged network service utility, which is the difference of video quality and energy expenditure. The problem formulation takes the following factors into account: time-varying grid electricity price, energy harvesting process, and different time scales of rate adaptation (RA), resource management, and electricity price fluctuation. We exploit Lyapunov optimization framework to decompose the problem into three subproblems: 1) RA subproblem; 2) battery energy management subproblem; and 3) joint power control and subchannel assignment subproblem. We propose an efficient online green video streaming algorithm to solve these subproblems. We analyze the stability of the proposed algorithm with respect to lengths of energy queue and user request queues. Extensive simulations are conducted and the results validate the efficiency of the proposed algorithm.

Incentive mechanism has been an important research direction in mobile crowdsensing. An effective incentive mechanism is critical to ensure the adequate number of participants/workers by providing them proper rewards. However, existing incentive mechanisms lack consideration on potential contributions of individual workers when recruiting new workers and retaining existing workers in the system. In this article, we propose a reverse auction-based incentive mechanism (RAIN), which considers participants' potential contributions when recruiting new workers, performing reverse auctions, and retaining existing workers. The design objective is to optimize the worker composition in the system while reducing the system cost. In RAIN, the potential contribution of a user to the system is measured as the degree at which the user's joining or staying in the system can remedy the inadequacy of workers for task auction/execution at the frequently visited locations of the user. We present design details of RAIN which includes selective worker recruitment, reverse auction based on biased bids, and selective retaining of auction losers, all based on individual users' potential contributions to the system. Extensive simulation results show that RAIN can effectively optimize the worker composition in a system and also effectively reduce the system cost.

Incentive mechanism design is a critical issue in mobile crowdsensing and a lot of work has been carried out. However, existing mechanisms in this area generally lack of consideration of individual worker/candidate's (potential) contribution to the system when recruiting new workers or when detaining existing workers. In this paper, we design a reverse auction based incentive mechanism. The design objective is to maximally reduce the system maintenance cost (including auction cost and recruitment cost) by optimizing the composition of workers in the system. For this purpose, in the recruiting process, candidates are queried in the descending order of their potential contributions to the system, while in the detaining process, likelydropping-out workers are rewarded with inner lottery whose amount is adjusted based on their usefulness to the system. In the auction process, prices are calculated based on workers' bids and also their usefulness to the system. We present detailed mechanism design. Simulation results show that our mechanism outperforms existing work.

Opportunistic routing has been a promising routing paradigm for the performance of a wireless sensor network (WSN) due to the broadcast and lossy characteristics of wireless channels. In this paper, we propose a Correlation Aware opportunistic Routing protocol CAR, which combines spatial correlation based data aggregation and opportunistic routing for achieving improved routing performance. The design behind CAR is to fully take advantage of the characteristic of spatial correlation among data sensed by neighbor sensor nodes and opportunistic nature for data reception in packet delivery in wireless channels. For this purpose, CAR first aggregate correlated data at selected forwarder and then opportunistically forward aggregated data toward the intended destination to reduce data redundancy. Extensive simulations show that CAR outperforms existing work in terms of data delivery cost and overall energy consumption.

The emerging wireless energy transfer technology based on Radio Frequency (RF) is a promising technology for wireless rechargeable sensor networks (WRSN) as it can charge sensor nodes simultaneously. In this paper, we use a mobile charger to stay at some locations and stay for certain time at each location to charge all the nodes in the network. We first define a power-charging function for the whole network and then get a set of candidate stop locations for the mobile charger by analyzing the property of this function. After the set of candidate locations are determined, we formulate two optimization problems: one is to minimize total charging time and another is to maximize the charging efficiency, subject to a charged energy threshold at each sensor node. Simulation results show that our method for choosing stop locations can greatly reduce the total charging time and improve charging efficiency.

ABSTRACT Routing in a low duty‐cycled wireless sensor network (WSN) has attracted much attention recently because of the challenge that low duty‐cycled sleep scheduling brings to the design of efficient distributed routing protocols for such networks. In a low duty‐cycled WSN, a big problem is how to design an efficient distributed routing protocol, which uses only local network state information while achieving low end‐to‐end (E2E) packet delivery delay and also high packet delivery efficiency. In this paper, we study low duty‐cycled WSNs wherein sensor nodes adopt pseudorandom sleep scheduling for energy saving. The objective of this paper is to design an efficient distributed routing protocol with low overhead. For this purpose, we design a simple but efficient hop‐by‐hop routing protocol, which integrates the ideas of multipath routing and gradient‐based routing for improved routing performance. We conduct extensive simulations, and the results demonstrate the high performance of the proposed protocol in terms of E2E packet delivery latency and packet delivery efficiency as compared with existing protocols. Copyright © 2014 John Wiley & Sons, Ltd.

Abstract Topology control is an efficient strategy for improving the performance of wireless ad hoc and sensor networks by building network topologies with desirable features. In this process, location information of nodes can be used to improve the performance of a topology control algorithm and also ease its operations. Many location‐based topology control algorithms have been proposed. In this paper, we propose two location‐assisted grid‐based topology control (GBP) algorithms. The design objective of our algorithm is to effectively reduce the number of active nodes required to keep global network connectivity. In grid‐based topology control, a network is divided into equally spaced squares (called grids). We accordingly design cross‐sectional topology control algorithm and diagonal topology control algorithm based on different network parameter settings. The key idea is to build near‐minimal connected dominating set for the network at the grid level. Analytical and simulation results demonstrate that our designed algorithms outperform existing work. Furthermore, the diagonal algorithm outperforms the cross‐sectional algorithm. Copyright © 2016 John Wiley & Sons, Ltd.

Battery recovery effect is a phenomenon that the available capacity of a battery could increase if the battery can sleep for a certain period of time since its last discharging. Accordingly, the battery can work for a longer time when it takes some rests between consecutive discharging processes than when it works all the time. However, this effect has not been considered in the design of energy-efficient topology control algorithms for wireless sensor networks. In this paper, we propose a distributed battery recovery effect aware connected dominating set constructing algorithm (BRE-CDS) for wireless sensor networks. In BRE-CDS, each network node periodically decides to join the connected dominating set or not. Nodes that have slept in the preceding round have priority to join the connected dominating set in the current round while nodes that have worked in the preceding round are encouraged to take sleep in the current round for battery recovery. Detailed algorithm design is presented. The computational complexity of BRE-CDS is deduced to be O(D2), where D is node degree. Simulation results show that BRE-CDS can significantly prolong the network lifetime as compared with existing work. Copyright © 2016 John Wiley & Sons, Ltd.

With the increasing popularity of wireless local area network infrastructure, Wi-Fi fingerprint based indoor positioning systems have received considerable attention in recent years. In the literature, most existing work in this area focuses on techniques that match the vector of radio signal strength (RSS) values reported by a mobile device to the fingerprints collected at predetermined reference points (RPs) by comparing the similarity (measured based on RSS difference) between them. However, these existing techniques fail to consider the fact that equal RSS differences at different RSS levels may not mean equal distances in reality. To address this issue, in this paper, we propose a feature scaling based k-nearest neighbor algorithm (FS-kNN) for improved localization accuracy. In FS-kNN, we build a novel RSS-based feature scaling model, which introduces signal-level-scaled weights in the calculation of effective signal distance between signal vector reported by mobile device and existing fingerprints. Experimental results show that FS-kNN can achieve an average error distance as low as 1.93 meters, which is superior to previous work.

In this paper, we design a novel lightweight ring-based routing protocol (LRRP) for wireless sensor network with mobile sinks (mWSN). The design objective is to significantly reduce the protocol overhead for route discovery and management while preserving high routing performance. For this purpose, LRRP builds a base ring by finding a shortest cycled path surrounding an artificially created topological hole in the network and, based on the base ring, it builds a ring-based structure to cover remaining nodes in the network and further assigns them ring IDs and virtual angles to ease the packet forwarding. LRRP works in a hybrid way for routing updates and packet forwarding. Specifically, each mobile sink (MS) dynamically chooses agent nodes, one on each ring, to proactively update its reachability as it moves. Each data packet is forwarded using ring-based forwarding along a pre-selected ring until reaching an MS or an agent node with path to an MS, from which the packet will be directly forwarded towards the MS. LRRP further considers how to achieve a good tradeoff between energy balancing among different rings and data path lengths. Extensive simulation results show that LRRP can significantly reduce the protocol overhead and achieve prolonged network lifetime as compared with existing work while achieving a very high packet delivery ratio.

In this paper, we propose a location-based friend-assisted coding-aware routing protocol (LFCR) for wireless multihop networks. To achieve improved network throughout, LFCR performs inter-flow network coding based routing with the assistance of location information. Specifically, LFCR combines friend-assisted path discovery and coding-aware routing. Further, when making decision on next hop selection, LFCR takes into account both coding opportunities and forwarding progress in next hop selection and attempts to make a good tradeoff between them. Simulation results show that LFCR significantly outperforms existing work in terms of network throughput, packet delivery ratio, and coding frequency.

Recently, much effort has been made for implementation of back-pressure scheduling in wireless networks. In this paper, we explore the implementation of back-pressure-based forwarding in wireless sensor networks. For this purpose, we propose Gradient-pressure, a practical Gradient-assisted anycast-based back-pressure framework for wireless sensor networks. Gradient-pressure introduces gradient information to assist transmission scheduling and realizes distributed anycast-based back-pressure scheduling on top of IEEE 802.11. Simulation results demonstrate that Gradient-pressure has high performance in terms of energy-use efficiency and goodput.

Network coding has been as a new coding paradigm that can significantly improve the throughput performance of a wireless multi-hop network. However, most previous studies either assume a fixed transmission power or do not take the impact of transmission power/rate to the network coding into consideration. Since in many scenarios, the selection of the transmission power/rate has big impact on network coding gains due to the fact the reception and overhearing probability, spatial reuse are both rely on transmission power/rate. Therefore, how to achieve high network throughput by appropriate selection of transmit power level and its corresponding packet transmit rate, network coding gain (if any) via localized network operations has been a critical issue in distributed multihop wireless networks, or alternatively, what is the best trade-off between the space-time resource (level of spatial reuse and transmission time) and the network coding gain? In this paper, our goal is to achieve the best trade-off between transmission power/rate and coding gains. Aiming at this, we propose a decentralized network coding aware power and rate control mechanism to enable each node to adjust its transmit power and data rate such that the network coding gains and the network throughput is maximized. Simulation results show that the proposed mechanism yields higher performance in network throughput as compared with existing work.

Low duty cycled scheduling can largely prolong the lifetime of a wireless sensor network (WSN) but also brings longer end-to-end (E2E) delivery delay. In this paper, our design objective is to pursue the near minimum E2E energy consumption subject to a desired success ratio that the E2E delay is below a delay bound. Accordingly, we design a Markov decision process based geographic routing protocol such that each relay node currently holding a packet makes localized forwarding decision on continuing waiting or transmitting immediately to the so far best forwarder candidate based only on local network state information. Simulation results show that the designed protocol can achieve expected success ratio subject to given delay bound and also high energy use efficiency.

In this paper, we propose an efficient network coding based back-pressure algorithm (NBP). NBP introduces the interflow network coding to improve the performance of the backpressure algorithm (a famous throughput-optimal cross-layer scheduling algorithm) for scheduling the transmissions of packets and also higher transmission efficiency. We theoretically prove that NBP can stabilize such networks. Simulation results demonstrate that NBP significantly outperforms traditional back-pressure algorithm in terms of packet delivery delay and average forwarding queue length.

Low duty cycled sleep scheduling can largely prolong the lifetime of a wireless sensor network (WSN). However, it also brings big challenge to the design of efficient routing protocols. Inappropriate selection of routes can largely offset the efficiency of a sleep scheduling mechanism. This paper aims to provide a simple but efficient forwarding discipline for packet routing in a low duty cycled WSN wherein each node chooses its wakeup slot in a pseudo-random manner. In such a duty-cycled WSN, a big problem is how a node can make a forwarding decision in order to obtain an acceptable end-to-end (E2E) delivery latency as well as high energy efficiency. In this paper, we design a multipath routing protocol that combines gradient information and predictable link delay to make the forwarding decision. Simulation results show that the designed protocol can obtain high performance in terms of delivery latency and energy efficiency compared with existing work.

ABSTRACT Node localization is essential to wireless sensor networks (WSN) and its applications. In this paper, we propose a particle swarm optimization (PSO) based localization algorithm (PLA) for WSNs with one or more mobile anchors. In PLA, each mobile anchor broadcasts beacons periodically, and sensor nodes locate themselves upon the receipt of multiple such messages. PLA does not require anchors to move along an optimized or a pre‐determined path. This property makes it suitable for WSN applications in which data‐collection and network management are undertaken by mobile data sinks with known locations. To the best of our knowledge, this is the first time that PSO is used in range‐free localization in a WSN with mobile anchors. We further derive the upper bound on the localization error using Centroid method and PLA. Simulation results show that PLA can achieve high performance in various scenarios. Copyright © 2011 John Wiley & Sons, Ltd.

In a wireless sensor network with mobile sinks, the maintenance of efficient data delivery structure often brings a large amount of control overhead, which may offset the benefit of introducing mobile sinks. In this paper, we propose a multi-stage data routing protocol (called MLRP) to address this problem. To reduce the protocol overhead, MLRP integrates layered Voronoi scoping and dynamic anchor selection. Such design is expected to greatly reduce the diffusion scope of sink movement updates and thus reduce the frequency at which the data delivery structure is refreshed. Simulation results show that MLRP can effectively reduce the protocol overhead while ensuring high packet delivery ratio as compared with existing work.