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Mobile ad-hoc cloud can exploit the computing resources (e.g., smartphones, vehicles, and unmanned systems) scattered in the mobile environment to form a self-organized ad-hoc local resource pool for providing opportunistic computing services. However, the highly dynamic and distributed characteristics of the mobile ad-hoc network environment bring great challenges in privacy and security in such opportunistic resource sharing. In this article, we first discuss the attractive features of blockchain for providing such resource sharing services in the mobile ad-hoc network environment in a secure and trustful way and then discuss the problems caused when using existing consensus protocols in such an environment. We accordingly devise a blockchain based trustful mobile ad-hoc cloud architecture, AdChain cloud. We describe the functions at different layers in this architecture, including the network layer, blockchain layer, and smart contract layer. To adapt to the high dynamics of the mobile ad-hoc network environment, we design a stability-aware consensus protocol at the blockchain layer. Simulation results show that our solution can achieve improved performance as compared with existing work.

Cache-enabled device-to-device (D2D) communication has emerged as a promising paradigm for caching popular contents at user devices and using D2D communication among adjacent users to relieve cellular traffic overload. To maximize traffic offloading, optimal content-selection for caching and helper-receiver matching are two key concerns. In this paper, we deal with the two key issues by leveraging user characteristics, which are crucial to D2D content sharing but often omitted by existing work in this field. We first propose a comprehensive model that profiles user characteristics including user preference, user activity degree, and user relationship. To provide an accurate estimation of user relationship, a new metric named expected correlation coefficient (ECC) is further proposed, which can reveal the potential offloading opportunities behind the contacts between users. We then design a novel user-centric caching policy that integrates the above-mentioned models into content caching policy to optimize the caching efficiency. Furthermore, we propose an online learning algorithm by leveraging the Combinatorial Multi-Armed Bandits (CMAB) method to achieve efficient helper-receiver matching among users. Experiments based on real-world traces and content requesting records were conducted. The results demonstrate that when compared with existing algorithms, our proposed algorithm improves the offloading ratio by nearly 10% and the system utility approximately by 20%.

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.

Sample scheduling is a crucial issue in wireless sensor networks (WSNs). The design objectives of efficient sample scheduling are in general two-folds: to achieve a low sample rate and also high sensing quality. Recently, compressive sensing (CS) has been regarded as an effective paradigm for achieving high sensing quality at a low sample rate. However, most existing work in the area of CS for WSNs use fixed sample rates, which may make sensor nodes in a WSN unable to capture significant changes of target phenomenon, unless the sample rate is sufficiently high, and thus degrades the sensing quality. In this paper, to pursue high sensing quality at low sample rate, we propose an adaptive CS based sample scheduling mechanism (ACS) for WSNs. ACS estimates the minimum required sample rate subject to given sensing quality on a per-sampling-window basis and accordingly adjusts sensors' sample rates. ACS can be useful in many applications such as environment monitoring, and spectrum sensing in cognitive sensor networks. Extensive trace-driven experiments are conducted and the numerical results show that ACS can obtain high sensing quality at low sample rate.

Replication routing can greatly improve the data delivery performance by enabling multiple replicas of the same packet to be transmitted towards its destination simultaneously. It has been studied extensively recently and is now a widely accepted rout- ing paradigm in delay tolerant networks (DTNs). However, in this field, the issue of how to maximize the utilization efficiency of lim- ited replication quota in a resource-saving manner and therefore making replication routing to be more efficient in networks w ith limited resources has not received enough attention. In this pa- per, we propose a DTN routing protocol with back-pressure based replica distribution. Our protocol models the replica distribution problem from a resource allocation perspective and it utilizes the idea of back-pressure algorithm, which can be used for providing efficient network resource allocation for replication quot a assign- ment among encountered nodes. Simulation results demonstrate that the proposed protocol significantly outperforms exist ing repli- cation routing protocols in terms of packet delay and delivery ratio.

Achieving high data quality and efficient network resource utilization is two major design objectives of wireless sensor networks WSNs. However, these two objectives are often conflictive. By allowing sensors to report sampled data at high rates, fine-grained data quality can be obtained. However, the limited resources of a WSN make it difficult to support very high traffic rate. Therefore, the capability of adaptively adjusting sensor nodes' traffic-generating rates on the basis of the availability of network resources and application requirements is critical. This issue has attracted much attention recently, and some work has been carried out.

Backpressure based scheduling has been considered as a promising technique for improving the throughput of a wide range of communication networks. However, this scheduling technique has not been well studied for heterogeneous wireless networks. In this paper, we propose a virtual-queue based backpressure scheduling (VQB) algorithm for heterogeneous multi-hop wireless networks. The VQB algorithm introduces a simple virtual queue for each flow at a node for backpressure scheduling, whose length depends on the cache size of the node. When calculating flow weights and making scheduling decisions, the length of a virtual queue is used instead of the length of a real queue. We theoretically prove that VQB is throughput-optimal. Simulation results show that the VQB algorithm significantly outperforms a classical backpressure scheduling algorithm in heterogeneous multi-hop wireless networks in terms of the packet delivery ratio, packet delivery time, and average sum of the queue lengths of all nodes per timeslot.

In this paper, we design a new virtual queue-based back-pressure scheduling algorithm (VBR) for achieving significant delay reduction in wireless sensor networks (WSN). Our algorithm design comes from an observation that classical back-pressure scheduling algorithm usually needs a long period of time to form a queue backlog-based gradient in a network, which decreases towards the sink in the network, before achieving stable packet delivery performance. To address this issue, VBR is designed to pre-build proper virtual queue-based gradient at nodes in a WSN, which is chosen to be a function of traffic arrival rate, link rate, and distance to sink, in order to be adaptive to different network and application environments while achieving high network performance. Moreover, the queue backlog differential between each pair of neighbor nodes is decided by their actual queue lengths and also their virtual queue lengths (gradient values). We prove that VBR can maintain back-pressure scheduling’s throughput optimality. Simulation result shows that VBR can obtain significant performance improvement in terms of packet delivery ratio, average end-to-end delay, and average queue length as compared with existing work.

Backpressure based scheduling has revealed remarkable performance in wireless multihop networks as reported in a lot of previous work. However, its lack of consideration on energy use efficiency is still an obstacle for backpressure based algorithms to be deployed in resource-constrained wireless sensor networks (WSNs). In this paper, we focus on studying the design of energy efficient backpressure based algorithm. For this purpose, we propose a gradient-assisted energy-efficient backpressure scheduling algorithm (GRAPE) for WSNs. GRAPE introduces a new link-weight calculation method, based on which gradient information and nodal residual energy are taken into account when making decisions on backpressure based transmission scheduling. According to the decisions made by this new method, packets are encouraged to be forwarded to nodes with more residual energy. We theoretically prove the throughput-optimality of GRAPE. Simulation results demonstrate that GRAPE can achieve significant performance improvements in terms of energy use efficiency, network throughput, and packet delivery ratio as compared with existing work.

Positioning technique has become an indispensable part of our daily lives. Compared with the well-solved positioning issue in outdoor areas, indoor positioning is still far from being well explored, and existing techniques in this area are still far from being sufficiently mature to be widely used in practice. Recently, visible light communication based indoor positioning, also referred to as visible light based positioning, has attracted great attention and much work has been carried out, which reveals remarkable positioning accuracy. In this article, we provide a comprehensive survey of visible light based positioning. We first introduce some fundamental issues in visible light based positioning and further categorize existing systems in this field according to different design criteria. We then give a comprehensive survey of state-of-the-art systems, and introduce how each of the systems works and discuss their merits and deficiencies. Finally, we discuss challenging issues in this area and also point out future directions.

Summary Back-pressure scheduling has been considered as a promising strategy for resource allocation in wireless multi-hop networks. However, there still exist some problems preventing its wide deployment in practice. One of the problems is its poor end-to-end (E2E) delay performance. In this paper, we study how to effectively use inter-flow network coding to improve E2E delay and also throughput performance of back-pressure scheduling. For this purpose, we propose an efficient network coding based back-pressure algorithm (NBP), and accordingly design detailed procedure regarding how to consider coding gain in back-pressure based weight calculation and how to integrate it into next hop decision making in the NBP algorithm. We theoretically prove that NBP can stabilize the networks. Simulation results demonstrate that NBP can not only improve the delay performance of back-pressure algorithm, but also achieve higher network throughput. Copyright © 2016 John Wiley & Sons, Ltd.