The neural-enhanced video streaming (NeVS) has been an emerging technique to integrate neural models into video codecs for higher streaming efficiency. The state-of-the-art methods, e.g., DeNC and Gemino, typically compress videos in RGB space and restore video quality via a neural enhancement model hosted on the external media server. However, these methods are not always accessible in resource-constrained edge environments due to their heavy reliance on the media server's computation, which undermines end-to-end performance and restricts NeVS's usage boundary. This limitation raises an interesting question: is it possible to make NeVS lightweight so that all neural codec operations can be handled directly by clients' edge devices? In this paper, we present the answer yes and develop a new plug-and-play module called DeNC++, which significantly improves the compression-restoration-overhead trade-off over existing methods. Our core design philosophy is to wrap all the codec operations within a latent semantic space, in which the original high-dimensional visual signals are efficiently embedded into low-dimensional semantic representations. With this fundamental transformation, DeNC++'s neural encoder introduces the triple semantic-bitwidth-resolution compression to effectively lower the streaming traffic. Meanwhile, we make DeNC++'s neural decoder aware of the perceptual loss caused by its encoder and design tiny generative models to guarantee high restoration quality. We also strictly restrict the runtime computational overhead and accelerate the neural enhancement process, making DeNC++ compatible with commodity edge devices. Real-world evaluations reveal that DeNC++ consistently provides higher restoration quality while achieving 24-55 times higher compression ratio and 5-7 times end-to-end speedup over the latest NeVS solutions.

Recent years have witnessed the rise of Neural-enhanced Video Streaming (NeVS), which integrates neural restoration models into video codecs for higher compression-restoration performance. Despite its benefit, existing work has not well explored the full potential of NeVS paradigm, due to: (1) post-streaming restoration by decoder while lacking the proactive collaboration of encoder, (2) end-to-end optimization based on conventional rate-distortion theory, which has been verified that low distortion is not always a synonym for high perceptual quality, and (3) coupled design for domain-specific tasks that cannot generalize to various video codecs. Observing these limitations, our objective is not to incrementally present an improved restoration model. Instead, we focus on the encoder-decoder synergy, i.e., the codec, which is non-trivial since it inherently strikes the rate-distortion-perception trade-off of NeVS. Aiming at this target, we propose the Diffusion-enhanced Neural Codec (DeNC), a plug-and-play module for current NeVS paradigm, to significantly reduce the required bitrates while preserving high perceptual quality of restored videos. Our key design is twofold. First, DeNC improves the encoder's compression efficiency by simultaneously reducing the resolution and color bit-depth of frame referencing. Second, DeNC empowers the decoder with perception-oriented restoration capability by making its diffusion-based restoration process aware of the encoder's compression conditions. Real-world evaluations show that DeNC improves compression ratios with nearly an order of magnitude and achieves much higher restoration quality (e.g., 93+ VMAF and 23% higher MOS) over the latest baselines.

Wireless networks are pivotal to modern communication, yet traditional address-centric protocols often fail to leverage the inherent advantages of wireless transmission potentials in dynamic edge environments. While Information-centric Networking (ICN) offers promising data-centric alternatives, the integration with wireless systems in resource-constrained edge settings faces critical challenges including channel instability, unreliable transmission, and fluctuating link quality. This paper introduces RENCA, a framework designed to enable seamless NDN-based wireless communication tailored for edge environments. RENCA addresses these challenges through three key innovations. First, it introduces a distributed collision avoidance strategy that leverages wireless broadcast properties to select high-quality communication pairs in real time, minimizing localized interference in wireless networks. Second, it incorporates a reliable transmission mechanism using Selective Negative Acknowledgement (SNAK) to ensure data integrity while aligning with content-centric principles in dynamic edge topologies. Third, it presents a dynamic Modulation Coding Scheme (MCS) adaptation that improves communication goodput in response to real-time channel fluctuations. We implement the entire RENCA protocol stack on real wireless hardware and drivers, and conduct extensive experiments over real wireless scenarios. The system achieves a 92.96% overall goodput improvement in multi-device edge environments compared to traditional protocols, demonstrating the usefulness of robust, high-performance communication for edge applications.

The rapid evolution of wireless edge computing faces fundamental limitations from connection-oriented protocols, particularly in dynamic scenarios with distributed edge environments. In this paper, we present SWICE, a novel transmission system with modified frame injection techniques that eliminates connection establishment overhead while ensuring reliable data delivery in wireless distributed edge environments. Our system introduces a connection-free measurement strategy that uses minimal packets to assess communication status while keeping device identities private. We formulate an edge node selection problem for efficiency and develop a heuristic algorithm for optimal data delivery. Additionally, we implement a reliability mechanism that combines adaptive retransmission to enhance communication stability. We conduct real-world experiments with commercially available Wi-Fi devices and modified wireless radios. The results show that SWICE achieves up to a 36.88 % increase in goodput compared to conventional transmission methods, demonstrating its effectiveness through real-world experiments.

Heterogeneous devices in edge computing bring challenges as well as opportunities for edge computing to utilize powerful and heterogeneous hardware for a variety of complex tasks. In this paper, we propose a service function chain placement strategy considering the heterogeneity of devices in deviceless edge computing environments. The service function chain system utilizes lightweight virtualization technologies to manage resources, considering the heterogeneity of devices to support various complex tasks, and offer low latency services to user requests. We propose an optimal service function chain placement problem minimizing the service delay and formulate it into a quasi-convex problem. We implement different edge applications that can be served by function chains and conduct extensive experiments over real heterogeneous edge devices. Results from the experiments and simulations show that our proposed service function chain scheme is applicable in edge environments, and perform well over services latency, resource utilization as well as the power consumption of edge devices.

HTTP/3 is designed to enhance performance and security by utilizing QUIC as its underlying transport protocol. Integrating HTTP/3 and its features can expand the application scope of ICN. Through an analysis of critical elements of HTTP/3, we implement it on NDN forwarding daemons and run applications in browsers to assess its capabilities and potential benefits in ICN environments. Our system demonstration illustrates compatibility with mainstream browsers while supporting ICN-based transmissions.

Information-centric Networking in edge computing environments exhibits the potential to significantly enhance the efficiency, reliability, and security of data transmission, making it a promising technology for future network deployments. However, the differences from traditional networks require applications to actively redevelop and redeploy onto edge devices, incurring additional costs for the proliferation of ICN applications. In this paper, we propose a system to adapt QUIC protocol over ICN networks in multi-access edge networks. The system aims to expand the application repertoire for ICN by providing a smooth transition of applications using QUIC to run on ICN networks. We design an ICN-QUIC conversion layer to manage the transmission of data from QUIC-based applications. We implement and evaluate the designed system. The experiment results show that, compared to existing networks, our system can enhance the transmission efficiency, i.e., up to 20 times better goodput in multicast situations, and achieves comparable results in unicast scenarios. We test the scalability of our system in real edge and wireless environments. We also deploy the real applications over the proposed system to demonstrate its compatibility in ICN and MEC environments.

Serverless Computing and Function-as-a-Service (FaaS) offer convenient and transparent services to developers and users. The deployment and resource allocation of services are managed by the cloud service providers. Meanwhile, the development of smart mobile devices and network technology enables the collection and transmission of a huge amount of data, which shifts tasks to the network edge for mobile users. In this paper, we propose a deviceless edge computing system targeting the mobility of end users using the data migration of virtual functions. We focus on the adjustment of migration among virtual functions to provide uninterrupted services to mobile users. We introduce the deviceless edge computing model and propose a seamless data migration scheme of virtual functions with limited involvement of function developers. We formulate the migration decision problem into integer linear programming and use receding horizon control (RHC) for online solutions. We implement the migration system to support delay-sensitive scenarios over real edge devices and develop a streaming game as the virtual function to test the performance. Extensive experiments in real scenarios exhibit the system has the ability to support high-mobility and delay-sensitive application scenarios. Extensive simulation results show the applicability of the proposed system over large-scale networks.

Introducing the emerging serverless paradigm into edge computing could avoid over- and under-provisioning of limited edge resources and make complex edge resource management transparent to application developers, which largely facilitates the cost-effectiveness, portability, and short time-to-market of edge applications. However, the computation/data dispersion and device/network heterogeneity of edge environments prevent current serverless computing platforms from acclimating to the network edge. In this paper, we address such challenges by formulating a container placement and data flow routing problem, which fully considers the heterogeneity of edge networks and the overhead of operating serverless platforms on resource-limited edge servers. We design an online algorithm to solve the problem. We further show its local optimum for each arriving container and prove its theoretical guarantee to the optimal offline solution. We also conduct extensive simulations based on practical experiment results to show the advantages of the proposed algorithm over existing baselines.

Innovative edge devices (e.g., smartphones, IoT devices) are becoming much more pervasive in our daily lives. With powerful sensing and computing capabilities, users can generate massive amounts of data. A new business model has emerged where data producers can sell their data to consumers directly to make money. However, how to protect the profit of the data producer from rogue consumers that may resell without authorization remains challenging. In this paper, we propose a smart-contract based protocol to protect the profit of the data producer while allowing consumers to resell the data legitimately. The protocol ensures the revenue is shared with the data producer over authorized reselling, and detects any unauthorized reselling. We also introduce a data relay process that can enhance data accessibility in wireless edge networks. We formulate a revenue sharing problem to maximize the profit of both the data producer and resellers/relayers. We formulate the problem into a two-stage Stackelberg game and determine a ratio to share the reselling revenue between the data producer and resellers/relayers. Extensive simulations show that with resellers and relayers, our mechanism can achieve up to 49.5 percent higher profit for the data producer and resellers/relayers.

Serverless Computing and Function-as-a-Service (FaaS) offer convenient and transparent services to developers and users. The deployment and resource allocation of services are managed by the cloud service providers. Meanwhile, the development of smart mobile devices and network technology enables the collection and transmission of a huge amount of data, which creates the mobile edge computing shifting tasks to the network edge for mobile users. In this paper, we propose a deviceless edge computing system targeting the mobility of end users. We focus on the migration of virtual functions to provide uninterrupted services to mobile users. We introduce the deviceless edge computing model and propose a seamless migration scheme of virtual functions with limited involvement of function developers. We formulate the migration decision problem into integer linear programming and use receding horizon control (RHC) for online solutions. We implement the migration system and algorithm to support delay-sensitive scenarios over real edge devices and develop a streaming game as the virtual function to test the performance. Extensive experiments in real scenarios exhibit the system has the ability to support high-mobility and delay-sensitive application scenarios. Extensive simulation results also show its applicability over large-scale networks.

Edge caching is an effective way to reduce congestion and latency in 5G networks. Non-volatile memory (NVM) devices are developing fast, with the potential of fast access, and higher endurance versus traditional storage devices, to further boost mobile data offloading efficiency in 5G networks. This paper studies how to effectively use the two-layer storage system (NVM-enhanced) in 5G edge caching. We first model an edge caching optimization problem with NVM storage devices included and develop a parallel distributed algorithm with guaranteed convergence in joint caching and routing decisions. A fully decentralized algorithm for scenarios without any coordination is further developed which also guarantees the convergence. Real-world trace-driven simulations and experiments over a small-scale system demonstrate that NVM significantly boosts the performance of edge caching and the proposed algorithms outperform the existing ones.

The blockchain is introduced as a safe and decentralized technology widely used in cryptocurrencies. It provides a distributed and disintermediation system to securely process and store transactions between peer devices. Traditionally, every transaction in the blockchain is broadcasted throughout the network, which leaves huge computational and communicational overhead to nodes. Nodes may refuse to forward transactions, thereby hindering the consensus of the blockchain. In this paper, we design a blockchain system with Incentive Transaction Forwarding (ITF). ITF allows nodes to share the revenue from transaction fees as the incentive for transaction forwarding. We propose a mechanism keeping the topology updated for computing incentive allocations. We develop an incentive allocation algorithm to distribute revenue among nodes that forward transactions. We analyze the security of ITF and prove that nodes cannot get unfair advantages in our system by common attacks. Extensive simulations show that our system can have fair incentive allocations for relay nodes and against several attacks from adversaries.

The fast development of mobile edge computing (MEC) and service virtualization brings new opportunities to the deployment of interactive applications, e.g., VR education, stream gaming, autopilot assistance, at the network edge for better performance. Ensuring quality of experience (QoE) for such services often requires the satisfaction of multiple quality of service (QoS) factors, e.g., short delay, high throughput rate, low packet loss. Nevertheless, existing mobile edge networks often fail to meet these requirements due to the mobility of end users and the volatility of network conditions. In this paper, we propose a novel scheme that both reduces delay and adjusts data throughput rate for QoE enhancement. We design an online service placement and throughput rate adjustment (SPTA) algorithm which coordinately migrates virtual services while tuning their data throughput rates based on real-time bandwidth fluctuation. By implementing a small-scale prototype supporting stream gaming at the edge, we show the necessity and feasibility of our work. Based on data from the experiments, we conduct real-world trace driven simulations to further demonstrate the advantages of our scheme over existing baselines.

Edge computing is becoming pervasive in our daily lives with emerging smart devices and the development of communication technology. Resource-rich smart devices and high-density supportive networks make data transactions prevalent over edge environments. To ensure such transactions are unmodifiable and undeniable, blockchain technology is introduced into edge environments. In this paper, we propose a hybrid blockchain system to enhance the security for transactions and determine the incentive for miners in edge computing environments. We propose a Proof of Work (PoW) and Proof of Stake (PoS) hybrid consensus blockchain system utilizing the heterogeneity of devices to adapt to the characteristic of edge environments. We raise the incentive assignment problem for a fair incentive to PoW miners. We formulate the problem and propose an iterative and another heuristic algorithm to determine the incentive that the miner will receive for a new block. We further prove that the iterative algorithm can obtain global optimal results. Numerical simulation results show that our proposed algorithm can give a reasonable incentive to miners under different system parameters in edge blockchain systems.

Edge devices with sensing, storage, and communication resources are penetrating our daily lives. These resources make it possible for edge devices to conduct data transactions (e.g., micro-payments, micro-access control). The blockchain technology can be used to ensure transaction unmodifiable and undeniable. In this paper, we propose a blockchain system that adapts to the limitations of edge devices. The new blockchain system can fairly and efficiently allocate storage resources on edge devices, which makes it scalable. We find the optimal peer nodes for transaction data storage and propose a recent block storage allocation scheme for quick retrieval of missing blocks. We develop data migration algorithms to dynamically reallocate data and block storage to adapt topology changes in the network. The proposed blockchain system can also reach consensus with low energy consumption in edge devices with a new Proof of Stake mechanism. Extensive simulations show that our proposed blockchain system works efficiently in edge environments. On average, the new system uses 18.4 percent less time and consumes 87 percent less battery power when compared with traditional blockchain systems.

Emerging virtual network functions (VNFs) bring new opportunities to network services on the edge within customers’ premises. Network services are realized by chained up VNFs, which are called service function chains (SFCs). These services are deployed on commercial edge servers for higher flexibility and scalability. Despite such promises, it is still unclear how to provide highly available and cost-effective SFCs under edge resource limitations and time-varying VNF failures. In this paper, we propose a novel Reliability-aware Adaptive Deployment scheme named RAD to efficiently place and back up SFCs over both the edge and the cloud. Specifically, RAD first deploys SFCs to fully utilize edge resources. It then uses both static backups and dynamic ones created on the fly to guarantee the availability under the resource limitation of edge networks. RAD does not assume failure rates of VNFs but instead strives to find the sweet spot between the desired availability of SFCs and the backup cost. Theoretical performance bounds, extensive simulations, and small-scale experiments highlight that RAD provides significantly higher availability with lower backup costs compared with existing baselines.

How to serve mobile users in rural areas by 5G networks is challenging due to the sparse distribution of base stations and poor connection to the cloud. Existing solutions focus on transmission frequency implementation such as frequency multiplexing, In this paper, we consider a decentralized caching scheme for two reasons. First, caching contents in the edge is an effective approach to reduce the transmission latency and improve the quality of service for mobile users. Second, the decentralized caching allows base stations to serve mobile users without any coordination of the cloud. Meanwhile, data privacy in the edge is critical for individual users. This paper aims to jointly determine the caching and routing policy in rural areas of 5G networks in a decentralized manner and simultaneously design a proper privacy-preserving mechanism. We tackle the challenges in two progressive steps. First, we design a decentralized algorithm with the convergence guarantee. Furthermore, we enhance the developed decentralized algorithm with a privacy-preserving mechanism based on (local) differential privacy and prove its privacy guarantee. We conduct extensive numerical simulations based on real-world traces to evaluate the proposed algorithms. Results highlight significant performance improvements compared to existing baselines.

Edge computing guides the collaborative work of widely distributed nodes with different sensing, storage, and computing resources. For example, sensor nodes collect data and then store it in storage nodes so that computing nodes can access the data when needed. In this paper, we focus on the quality of service (QoS) in storage allocation in edge networks. We design a reputation mechanism for nodes in edge networks, which enables interactive nodes to evaluate the quality of service for reference. Each node publicly broadcasts a personal reputation list to evaluate all other nodes, and each node can calculate the global reputation of all nodes by aggregating personal reputations. We then propose a storage allocation algorithm that stores data to appropriate locations. The algorithm considers fairness, efficiency, and reliability which is derived from reputations. We build a novel Proof-of-Reputation (PoR) blockchain to support consensus on the reputation mechanism and storage allocation. The PoR blockchain ensures safety performance, saves computing resources, and avoids centralization. Extensive simulation results show our proposed algorithm is fair, efficient, and reliable. The results also show that in the presence of attackers, the success rate of honest nodes accessing data can reach 99.9%.

Distributed edge caching has drawn great attention with the fast development of smart edge devices. Caching popular contents in the edge can reduce latency and improve the quality of service of edge mobile users. Meanwhile, the data privacy in the edge is critical to preserve the privacy of individual users and devices. How to jointly determine the caching and routing policy in the edge network in a distributed manner and simultaneously design the proper privacy preserving mechanism are challenging. We tackle these challenges in two progressive steps. First, we design a distributed algorithm which can achieve the global optimum. Second, we propose a privacy-preserving mechanism based on differential privacy and prove the privacy guarantee. We conduct extensive numerical simulations based on real-world requests to evaluate the performance of the proposed distributed algorithm and the privacy mechanism. Results highlight a significant improvement of the proposed distributed algorithm while only up to 10.1% of the total serving cost increased by the privacy mechanism.

Innovative edge devices (e.g., smartphones, IoT devices) are becoming much more pervasive in our daily lives. With powerful sensing and computing capabilities, users can generate massive amounts of data. A new business model has emerged where data producers can sell their data to consumers directly to make money. However, how to protect the profit of the data producer from rogue consumers that may resell without authorization remains challenging. In this paper, we propose a smart-contract based protocol to protect the profit of the data producer while allowing consumers to resell the data legitimately. The protocol ensures the revenue is shared with the data producer over authorized reselling, and detects any unauthorized reselling. We formulate a fair revenue sharing problem to maximize the profit of both the data producer and resellers. We formulate the problem into a two-stage Stackelberg game and determine a ratio to share the reselling revenue between the data producer and resellers. Extensive simulations show that with resellers, our mechanism can achieve higher profit for the data producer and resellers.

The fast development of virtual network functions (VNFs) brings new opportunities to network service deployment on edge networks. For complicated services, VNFs can chain up to form service function chains (SFCs). Despite the promises, it is still not clear how to backup VNFs to minimize the cost while meeting the SFC availability requirements in an online manner. In this paper, we propose a novel self-adapting scheme named SAB to efficiently backup VNFs over both the edge and the cloud. Specifically, SAB uses both static backups and dynamic ones created on the fly to accommodate the resource limitation of edge networks. For each VNF backup, SAB determines whether to place it on the edge or the cloud, and if on the edge, which edge server to use for load balancing. SAB does not assume failure rates of VNFs but instead strives to find the sweet point between the desired availability of SFCs and the backup cost. Both theoretical performance bounds and extensive simulation results highlight that SAB provides significantly higher availability with lower backup cost compared with existing baselines.

Edge computing is becoming pervasive in our daily lives with emerging smart devices and the development of communication technology. Resource-rich smart devices and high-density supportive networks make data transactions prevalent over edge environments. To ensure such transactions are unmodifiable and undeniable, blockchain technology is introduced into edge environments. In this paper, we propose a hybrid blockchain system in edge environments to enhance the security for transactions and determine the incentive for miners. We propose a Proof of Work (PoW) and Proof of Stake (PoS) hybrid consensus blockchain system utilizing the heterogeneity of devices to adapt to the characteristic of edge environments. We raise the incentive assignment problem that gives the corresponding PoW miner when a new block generates. We further formulate it into a two-stage Stackelberg game. We propose an algorithm and prove that it can obtain the global optimal results for the incentive that the miner will receive for a new block. Numerical simulation results show that our proposed algorithm can give reasonable incentive to miners under different system parameters in edge blockchain systems.

Edge caching is an effective approach to improve the quality of service for mobile users and therefore a critical component for 5G networks. Despite the importance, it is not clear how to determine which contents to cache and how to the serve requests in 5G networks to minimize the total operational cost in a distributed and online manner, especially when some mobile users can be served by multiple small base stations. In this paper, we formulate an optimization problem to jointly decide the caching policy and the routing decision. There are two challenges: the need for distributed control and the lack of future information. We therefore develop an online distributed algorithm with provable performance guarantees in terms of convergence and competitive ratio compared to the offline optimal solution. Numerical simulations based on real-world traces highlight the significant performance improvement compared to existing baselines.

Edge devices with sensing, storage, and communication resources (e.g., smartphones, tablets, connected vehicles, and IoT nodes) are increasingly penetrating our daily lives. Many novel applications can be created through sharing data among nearby peer edge devices. In such applications, caching data at some edge devices can greatly improve data availability, retrieval robustness, and delivery latency. In this paper, we study the unique problem of caching fairness in edge computing environments. Due to the heterogeneity of peer edge devices, load balance is a critical issue that affects the fairness in caching. We propose fairness metrics to characterize this issue and formulate the caching fairness problem as an integer linear programming problem, which is shown as the summation of multiple Connected Facility Location (ConFL) problems. We provide an approximation algorithm by leveraging an existing ConFL approximation algorithm, and prove that it preserves a 6.55 approximation ratio. We further develop a distributed algorithm where devices exchange data reachability information and identify popular candidates as caching nodes. Finally, we update the fairness metric and apply it to algorithms for making continuous caching decisions overtime. Our extensive evaluation results show that compared with existing caching algorithms for wireless networks, our proposed algorithms significantly improve the data caching fairness while keeping the contention induced latency comparable to the best existing algorithms.

Edge devices with sensing, storage, and communication resources are penetrating our daily lives. These resources make it possible for edge devices to conduct data transactions (e.g., micro-payments, micro-access control). The blockchain technology can be used to ensure transaction unmodifiable and undeniable. In this paper, we propose a blockchain system that adapts to the limitations of edge devices. The new blockchain system can fairly and efficiently allocate storage resources on edge devices, which makes it scalable. We find the optimal peer nodes for transaction data storage in the blockchain, and propose a recent block storage allocation scheme for quick retrieval of missing blocks. The proposed blockchain system can also reach mining consensus with low energy consumption in edge devices with a new Proof of Stake mechanism. Extensive simulations show that our proposed blockchain system works efficiently in edge environments. On average, the new system uses 15% less time and consumes 64% less battery power when compared with traditional blockchain systems.

This paper considers how to cache popular contents and load balancing in 5G networks to minimize the total operating cost. Specifically, popular contents requested by mobile users (MUs) are cached in small base stations (SBSs) to serve them with better quality and lower cost because the SBSs are often much closer to MUs than the base station (BS). Due to limited caching capacity and bandwidth of SBSs, the caching policy and load balancing algorithm need to be carefully designed jointly and dynamically over time. In this paper, we formulate the joint content placement and load balancing by an online optimization problem. This problem is challenging because of the integer constraint in content placement and the lack of future information. We tackle the challenges in two progressive steps. First, we propose a primal-dual algorithm to solve the problem efficiently and prove it always achieves the optimal cost assuming all system information is available. Then we integrate promising online optimization algorithms with the proposed primal-dual algorithm so that only limited short-term predictions are needed. Theoretical performance bounds are also derived. We conduct extensive numerical simulations to evaluate the performance of proposed algorithms. Results highlight that the proposed online algorithms can reduce the system cost significantly (by as much as 27%) compared to the existing solutions and perform similarly to the offline optimal solution.

Emerging innovative edge devices like drones, self-driving cars, phones/tablets and IoT nodes are revolutionizing our daily lives. Caching data among peer edge devices enables data sharing needed in many applications. In such applications, network scalability and node mobility bring many challenges. They change the topology and the resources in the network and make the network less robust. In this paper, we propose peer data caching strategies that consider the scale and mobility of these increasingly popular edge devices. We propose a grouping method creating a layered design to reduce the number of entities in each layer. We propose inter-group and intra-group optimization problems which proactively cache data onto best places to support robust and fast data access. We develop a 7-approximation algorithm for inter-group optimization and use uncapacitated facility location problems to solve intra-group optimization. We also transform the mobility of nodes into node behaviors to reduce the impact of mobility on the network. Our extensive simulation results show that our proposed strategies can apply to large-size and high-mobility networks, while achieving satisfactory results for data access.

Edge devices (e.g., smartphones, tablets, connected vehicles, IoT nodes) with sensing, storage and communication resources are increasingly penetrating our environments. Many novel applications can be created when nearby peer edge devices share data. Caching can greatly improve the data availability, retrieval robustness and latency. In this paper, we study the unique issue of caching fairness in edge environment. Due to distinct ownership of peer devices, caching load balance is critical. We consider fairness metrics and formulate an integer linear programming problem, which is shown as summation of multiple Connected Facility Location (ConFL) problems. We propose an approximation algorithm leveraging an existing ConFL approximation algorithm, and prove that it preserves a 6.55 approximation ratio. We further develop a distributed algorithm where devices exchange data reachability and identify popular candidates as caching nodes. Extensive evaluation shows that compared with existing wireless network caching algorithms, our algorithms significantly improve data caching fairness, while keeping the contention induced latency similar to the best existing algorithms.

The proliferation and daily congregation of modern mobile devices have created abundant opportunities for peer edge devices to share valuable data with each other. The short contact durations, relatively small sharing sizes, and uncertain data availability, demand agile, light weight peer based data sharing. In this paper, we propose Peer Data Sharing (PDS) that enables edge devices to discover which data exist in nearby peers, and retrieve interested data robustly and efficiently. PDS uses novel lingering queries, mixedcast and en-route message rewriting techniques to minimize redundant transmissions and maximize opportunistic overhearing thus caching in data discovery and retrieval. Extensive evaluations based on an Android prototype show that PDS discovers and retrieves almost 100% data in tens of seconds, and remains robust despite wireless contention, simultaneous consumer requests and user mobility.

As the indoor location-based services are widely used in daily life, we all call for precise localization and low energy consumption application. Besides, getting location information in a short re-sponse time remains a problem in indoor navigation. In this pa-per, we present an accurate and efficient indoor navigation sys-tem called AENS, which only needs available sensors in off-the-shelf smartphones such as accelerometer and gyroscope, completely avoid using energy hungry WiFi module. Combined with simple local map information, AENS is able to guide user to find destinations. The System utilizes those sensors to do Dead-Reckoning dynamically and in real-time, then fitting the predict-ed trajectories to the map information. We evaluate the system in our university library. The result shows that AENS successfully guides the tester to the specified destination, and for every critical node the average localization error is within one-meter.