Accurate radio map reconstruction plays a vital role in supporting enhanced Ultra-Reliable Low-Latency Communication (eURLLC), where reliable signal strength estimation enables efficient planning and link adaptation. However, reconstructing high-resolution radio maps from sparse and irregular measurements remains a significant challenge due to complex wireless propagation and limited observability. In this work, we propose KR-MAE, an image-driven framework based on masked autoencoders that formulates radio map reconstruction as a distribution-level regression task. By leveraging the global modeling capability of transformers, KR-MAE captures spatial signal structures from partial observations. It incorporates Kullback–Leibler(KL) divergence to align distributional characteristics between predictions and ground truth, and applies focal loss to focus on challenging regions with high reconstruction uncertainty. Experimental results on real-world wireless datasets show that KR-MAE achieves up to 51.92% lower reconstruction error compared to deep learning baselines. The proposed method demonstrates strong potential to support URLLC-aware wireless resource management, link reliability estimation, and large-scale radio environment modeling.

This paper presents a novel semantic communication system for efficient and resilient control signal transmission in unmanned aerial vehicles (UAVs). Traditional bit-level transmission methods face challenges under poor channel conditions and dynamic environments, where packet loss and the high dimensionality of control signals impact operational stability. To address these challenges, we propose a long short-term memory (LSTM) based semantic encoding and decoding framework that compresses essential control information. Additionally, a trajectory prediction model is integrated into the semantic framework to refine control signals, thereby enhancing accuracy. Our design includes an encoder at the ground control station (GCS) and a lightweight decoder onboard the UAV, making it suitable for resource-constrained UAVs without onboard control signal processing. Performance evaluations across varying channel conditions demonstrate that the proposed semantic communication method reduces trajectory mean squared error by over 60% and saves communication costs by at least 37% compared to traditional bit-level communication approaches.

Emerging Blockchain-empowered Federated Learning (BCFL) technology combines the decentralized security of blockchain with the privacy protection of federated learning. BCFL addresses the issue of single points of failure in centralized systems, making it an increasingly popular solution. However, current consensus mechanisms, such as Proof of Work (PoW), Proof of Stake (PoS), and Practical Byzantine Fault Tolerance (PBFT), lead to challenges such as high computational costs and limited scalability. This paper proposes a Batch Zero-Knowledge Proof-based practical Byzantine fault-tolerant (BZ-BFT) consensus mechanism for BCFL to enhance efficiency and reliability. By integrating Zero-Knowledge Proof (ZKP), our approach enables the verification of the primary node's proposal without revealing information from other network nodes, thereby ensuring the credibility of the aggregated results. To address the high computational overhead associated with ZKP, we present a batch quantization preprocessing technique called BatchZKP. Our proposed BZ-BFT reduces initialization, proof generation, and verification time by$97.81 \%, 70.0 \%$, and 47.64 %, respectively, significantly boosting BCFL system efficiency and reliability. Additionally, our approach reduces communication complexity from$O\left(n^{2}\right)$to$O(n)$and enhances Byzantine fault tolerance to${1/2}$.

Achieving ultra-reliable and low-latency communication (URLLC) in integrated terrestrial networks (TNs) and non-terrestrial networks (NTNs) is essential for the advancement of 6G networks. However, there is a significant lack of theoretical tools for analyzing probabilistic end-to-end (E2E) delay in such integrated networks. This analysis is crucial for estimating URLLC delay threshold violations and ensuring compliance with URLLC delay requirements. To address this gap, this paper introduces a systematic approach based on stochastic network calculus (SNC) utilizing moment-generating functions (MGFs) to compute the E2E delay violation probability for target traffic in unmanned aerial vehicles (UAVs) assisted integrated TNs and NTNs. The unique aspects of this scenario include two components: (1) TNs using conventional ground base stations, and (2) NTNs employing UAVs to relay data to ground base stations or high-altitude platform stations (HAPS). Our approach enhances the calculation of MGFs for service processes by considering transmission failures and blockages over sub-6 GHz and mmWave fading channels. This improvement offers a more precise performance characterization and facilitates the derivation of a closed-form expression for the upper-bounded statistical delay violation probability. Numerical results confirm that UAVs are advantageous in ensuring E2E delay guarantees by facilitating line-of-sight (LOS) transmission. Additionally, we provide insightful observations for resource allocation strategies in UAV-assisted integrated TNs and NTNs.

This demo presents a novel implementation of physical layer key generation (PLKG) in UAV communication systems by integrating deep learning for key reconstruction. Secure communication is a significant challenge in UAV networks due to their high mobility, frequent topology changes, and vulnerability to eavesdropping. Traditional cryptographic methods are inefficient in such dynamic environments because of high computational costs and latency. Our approach leverages multimodal learning to enhance resilience against Doppler effects and dynamic channel variations. The demonstration showcases a working prototype that extracts channel state information (CSI), predicts UAV trajectory, and reconstructs cryptographic keys with improved consistency and lower mismatch rates compared to conventional methods. Our system utilizes ESP32 microcontrollers for real-time CSI acquisition and Raspberry Pi 4 for deep learning-based processing. We provide a graphical user interface (GUI) that visualizes real-time CSI fluctuations and reconstructed key bits, demonstrating the framework’s resilience to dynamic channel variations.

This paper introduces the first implementation of physical layer key generation (PLKG) on a real-world unmanned aerial vehicle (UAV) platform. To tackle the unique challenges of high mobility and dynamic communication environments in Internet of Drones (IoD) networks, we propose a novel multimodal learning framework for enhancing PLKG in UAV-to-ground communications. Static channel state information (CSI) features and dynamic UAV trajectory data are extracted using convolutional neural networks (CNN) and long short-term memory (LSTM) networks, respectively. Leveraging conditional embedding techniques, the predicted UAV position and velocity are integrated into the input space of the key reconstruction network as conditional features. The trained network serves as a Doppler-resilient feature extraction mapping function, thus achieving robust and consistent key generation under varying mobility conditions. Experimental results demonstrate lower key mismatch rates and higher reliability compared to existing CSI-based key generation methods.

Accurate and reliable localization is a prerequisite for unmanned aerial vehicle (UAV) swarm applications. However, conventional GPS or RF-based localization systems often do not function effectively in highly dynamic and unstable mobile ad-hoc environments. This paper proposes a new approach to localize UAVs accurately in unknown communication environments with anomalous GPS reception, based on the received signal strength (RSS) between UAVs. The proposed approach is non-trivial, given the combinational nature of the considered problem and the requirement of high localization accuracy in the UAV application scenario. The key idea of the proposed approach is to solve the position mapping problem by refining a convex relaxation formulation that considers whether the target to be localized is inside or outside the convex hull formed by the anchors. In addition, a variational graph autoencoder is utilized to learn the latent representations for the undirected graph formed from the estimated position, which is then used to calculate the anomaly score. The optimal anchor node selection is obtained by solving a fractional knapsack problem that takes into account the anomaly score of different anchor combinations. Simulation results demonstrate that the proposed approach achieves higher detection and localization accuracy and is more robust to RSS measurement errors compared to the baseline schemes.

In the post-fifth generation (5G) era, escalating user quality of service (QoS) strains terrestrial network capacity, especially in urban areas with dynamic traffic distributions. This paper introduces a novel cooperative unmanned aerial vehicle relay-based deployment (CUD) framework in satellite air-ground integrated networks (SAGIN). The CUD strategy deploys an unmanned aerial vehicle-based relay (UAVr) in an amplify-and-forward (AF) mode to enhance user QoS when terrestrial base stations fall short of network capacity. By combining low earth orbit (LEO) satellite and UAVr signals using cooperative diversity, the CUD framework enhances the signal to noise ratio (SNR) at the user. Comparative evaluations against existing frameworks reveal performance improvements, demonstrating the effectiveness of the CUD framework in addressing the evolving demands of next-generation networks.

In this article, we consider multiple solar-powered wireless nodes (WNs) which utilize the harvested solar energy to transmit collected data to multiple unmanned aerial vehicles (UAVs) in the uplink. In this context, we jointly design UAV flight trajectories, UAV-node user association, and uplink power control to effectively utilize the harvested energy and manage co-channel interference within a finite time horizon. The design goal is to ensure the fairness of WNs by maximizing the worst user rate. The joint design problem is highly nonconvex and requires causal (future) knowledge of the instantaneous energy state information (ESI) and channel state information (CSI), which are difficult to predict in reality. To overcome these challenges, we propose an offline method based on convex optimization that only utilizes the average ESI and CSI, where line-of-sight (LOS) and non-LOS (NLOS) channels are considered. The problem is solved by three convex subproblems with successive convex approximation (SCA) and alternative optimization. We further design an online convex-assisted reinforcement learning (CARL) method based on real-time environmental information. An idea of multi-UAV regulated flight corridors, based on the optimal offline UAV trajectories, is proposed to avoid unnecessary flight exploration by UAVs and enables us to improve the learning efficiency and system performance, as compared with the conventional reinforcement learning (RL) method. Computer simulations are used to verify the effectiveness of the proposed methods. The proposed CARL method provides 25% and 12% improvement on the worst user rate over the offline and conventional RL methods.

This article concerns the problem of the trajectory design and base station (BS) association for cellular-connected unmanned aerial vehicles (UAVs). To support safety-critical functions, one primary requirement for UAVs is to maintain reliable cellular connectivity at every time instant during the flight mission. Since the antenna gain of a ground BS (GBS) changes with the position of the UAV, the UAV-GBS association strategy should be jointly considered with the trajectory design, which has not been studied in the prior arts. In this article, we first formulate the problem of joint BS association and trajectory design with the objective of minimizing the mission completion time under a connectivity outage constraint. Then, a deep learning framework is proposed to solve the formulated nonconvex optimization problem in a decoupled manner. For the UAV-GBS association strategy, the signal strength radio map of a given area is constructed, which is used to train a deep neural network (DNN) to approximate the nonlinear mapping from the UAV position to the optimal GBS. To tackle the high complexity due to the coupled decision variables of GBS association and UAV movement, a novel deep reinforcement learning (DRL) approach is developed to learn the optimal trajectory, in which the UAV can learn from its own past good experiences. Our simulation results confirm the superiority of the proposed DRL approach compared to the conventional DRL approaches in terms of the trajectory length. Additionally, it is demonstrated that the nearest association scheme fails to provide reliable cellular connections, whereas our proposed approach can ensure strong connectivity with the GBS during the whole trajectory.

In this paper, we exploit the deep learning based technologies to mitigate the impact of unmanned aerial vehicle (UAV) jittering on wireless sensing performance. In recent years, UAV has been widely utilized for remote sensing applications due to its high flexibility and maneuverability. However, the mobility and vibration of the UAV's body may cause the jittering effect which can severely degrade the sensing performance. To our best knowledge, the impact of UAV jittering has not been fully examined in literature so far. To alleviate this problem, we propose to leverage adversarial denoising autoencoder (ADAE) for corrupted signal reconstruction. To validate the effectiveness of our proposed scheme, we consider a device-free human sensing scenario in which a UAV is used to sense surrounding human activity by analyzing the received signal strength (RSS). Experiments demonstrate that the proposed ADAE based scheme can effectively reduce the impact of UAV jittering, recovering up to 97% of the performance loss due to the UAV jittering.

This paper investigates the potentials of utilizing artificial intelligence (AI) based edge caching in the next generation of vehicular mobile networks. In recent years, vehicle-to-everything (V2X) has been a research focus, which enables the exchange of information between the vehicles and the outside world. To integrate vehicular networks and cellular radio technology, cellular-V2X (C-V2X) was proposed in 3GPP release 14. Further, mobile edge caching is regarded as an effective technique to allow local data access, which can support the low latency requirement of the V2X use cases. With the advance of AI technologies such as deep learning, there has been increasing demand in inference and learning from big vehicular data. In this paper, we present the detailed architecture of AI-based edge caching in vehicular networks with misbehaving vehicle detection as an illustrative case. Performance results are provided to investigate the benefit of the proposed architecture. Finally, we highlight the potential research directions.

This paper investigates the problem of cache node placement and selection with the coexistence of unmanned aerial vehicles (UAVs) cache and device- to-device (D2D) cache in mobile networks. In recent years, caching popular content in UAV base stations has received growing interests as a promising solution to improve communication performances. With the agility and mobility features, the dynamic movement of cache-enabled UAV should be further designed to increase the cache-aided throughput. Different from the conventional caching approaches assuming ground users remain static, we consider the dynamic movement design of UAV to maximize the cache- aided throughput taking into account the movement of ground users. As the formulated optimization problem is NP-hard, we propose a mobility-aware probabilistic caching algorithm in which K-means clustering is utilized to obtain the partition of ground users. Simulation results show that the proposed algorithm notably outperforms the pure D2D cache scheme (without UAV caching) in different cases.

Unmanned aerial vehicles (UAVs), aka drones, are widely used civil and commercial applications. A promising one is to use the drones as relying nodes to extend the wireless coverage. However, existing solutions only focus on deploying them to predefined locations. After that, they either remain stationary or only move in predefined trajectories throughout the whole deployment. In the open outdoor scenarios such as search and rescue or large music events, etc., users can move and cluster dynamically. As a result, network demand will change constantly over time and hence will require the drones to adapt dynamically. In this paper, we present a proof of concept implementation of an UAV access point (AP) which can dynamically reposition itself depends on the users movement on the ground. Our solution is to continuously keeping track of the received signal strength from the user devices for estimating the distance between users devices and the drone, followed by trilateration to localise them. This process is challenging because our on-site measurements show that the heterogeneity of user devices means that change of their signal strengths reacts very differently to the change of distance to the drone AP. Our initial results demonstrate that our drone is able to effectively localise users and autonomously moving to a position closer to them.

With the advantages of agility and mobility, unmanned aerial vehicles (UAVs) have been widely applied for various civil and military missions. To dynamically control and monitor UAV, it is necessary to broadcast their location information. However, flying in the aerial environment and the fixed operation location also make UAV communications more vulnerable to privacy attacks. In this paper, we present the machine learning (ML)-based attack of UAV-based wireless networks when an attacker can obtain both plaintext and ciphertext. The collected plaintext-ciphertext pairs can be used to train an ML classifier which can help decrypt the UAV messages. By simulations, we show that a simple neural network (NN) can decrypt UAV location data with high probability. Finally, we conclude the work and present a network coding based encryption scheme as our future research direction.

This paper proposes an enhanced secure pseudonym scheme to protect the privacy of cloud data in Internet of Drones (IoD). Nowadays, drones equipped with cameras can provide surveillance and aerial photography applications. Unlike the video devices, personal drones with high mobility can track and follow an individual, causing both the identity and location privacy issues. However, IoD devices cannot implement complex cryptographic schemes because of limited computing power. To this end, we develop a secure light-weight network coding pseudonym scheme. Our designed two-tier network coding can decouple the stored IoD cloud data from the owner's pseudonyms. Therefore, our proposed network coding-based pseudonym scheme can simultaneously defend against both outside and inside attackers. We implement our proposed two-tier light-weight network coding mechanism when facing untrusted cloud database. Compared to the computationally secure hash-based pseudonym scheme, our proposed scheme achieves the highest unconditional security level, but also can reduce more than 90% of processing time as well as 10% of energy consumption.

Software defined networking (SDN) enables network function programmability for ease of configuration and maintenance, and also allows network administrators to change traffic rules on the fly. However, denial of service (DoS) attacks pose security challenges on the centralized control plane of SDN. Although the transport layer security (TLS) can help secure the control plane, it is computationally intensive, complex to configure, and not mandatory in OpenFlow protocol. In this paper, we present a lightweight authentication solution, called hidden authentication (HiAuth), to protect the SDN controller by hiding the identities of the forwarding devices into the control packets via efficient bitwise operations. HiAuth is the first to incorporate information hiding techniques into OpenFlow to provide security against DoS attacks. HiAuth exploits the IP identification field of IPv4 and the transaction identification field of OpenFlow in two authentication schemes. The experimental results show that HiAuth can effectively mitigate intruder DoS attacks and provide high undetectability to attackers.

In this paper, we present the overflow problem of a network coding storage system (NCSS) when the encoding parameters and the storage parameters are mismatched. The overflow problem of the NCSS occurs because the network-coded encryption yields extended coded data, resulting in high storage and processing overhead. To avoid the overflow problem, we propose an overflow-avoidance NCSS scheme that takes account of security and storage requirements in both encoding and storage procedures. We provide the analytical results of the maximum allowable stored encoded data under the perfect secrecy criterion. The design guidelines to achieve high coding efficiency with the lowest storage cost are also presented.

This paper proposes a traffic-aware load balancing scheme for machine-to-machine (M2M) networks using software-defined networking (SDN). Load balancing techniques are essential for M2M networks to relieve the heavy loading caused by bursty traffic. Leveraging the capability of SDN to monitor and control the network, the proposed load balancing scheme can satisfy different quality of service requirements through traffic identification and rerouting. Experimental results show that the proposed scheme can reduce service response time up to 50% compared to the non-SDN load balancing scheme.

In future 5G communication systems, the mm-wave transmission is considered as a key solution to fulfill the growing demands in mobile traffic. To overcome the high propagation loss experienced at high frequencies, mm-wave transmission relies heavily on highly directional antennas. Because directional transmission is sensitive to position changes, the existing handover strategies which only consider the instantaneous received signal strength fails to provide robust connections for moving users. In this paper, we propose an improved handover strategy for mm-wave mobile networks taking the connection robustness issue into account. The connection robustness is evaluated in terms of the effective beam coverage probability, which is defined as the probability that a user stays in the coverage area of the same beam after a certain time interval. We show that the proposed handover strategy can reduce handover frequency up to 35% with negligible throughput loss compared to the existing signal strength based scheme.

This paper proposes a resource reservation scheme to reduce random access delay for Ultra-Reliable and Low-Latency Communication (URLLC) traffic. URLLC has been proposed as one of the key usage scenarios in the fifth generation (5G) of mobile networks. The primary requirement for providing URLLC services is the delay in the control plane being shorter than 10 ms. Clearly, it is challenging to achieve such the stringent delay requirement since the bursty URLLC requests can cause network congestion in the random access phase. To reduce the random access delay, we propose to reserve random access resources for URLLC during the initial random access transmission. The design criteria of random access and an analytical model for different reservation policies are presented in this paper. Simulation results show that the existing random access scheme fails to meet the delay requirement for URLLC traffic, but our proposed reservation scheme can meet the 10 ms delay requirement with 95% confidence.

This paper investigates the impact of request aggregation on delay performance in group-based machine type communications (MTC). To alleviate the network congestion caused by concurrent transmissions from massive MTC devices, group-based MTC with request aggregation are shown to be one of the effective schemes. In group-based MTC, the devices are grouped into clusters with a dedicated cluster head (CH) which aggregates the requests sent by the cluster members. Hence, the amount of concurrent transmissions for connection establishment to the base station (BS) can be reduced. However, how to determine the amount of aggregated requests in one CH-to-BS transmission, namely aggregation factor, is still an open issue. To this end, we formulate an optimal aggregation problem to minimize the request delay subject to a drop ratio constraint. Based on our analytical and simulation results, we present the performance curves to relate the aggregation factor and the request delay as well as the drop ratio. Consequently, we suggest the optimal aggregation factors for MTC applications requiring ultra-low latency and ultra-reliable delivery.

With the great potential in flexible network provisioning adapted for various quality of service (QoS) requirements, software-defined network (SDN) has been considered as one of the most promising network architectures in the next generation mobile networks. However, the rule management in SDN-enabled mobile networks becomes a challenging task due to rapid topology changes caused by high user mobility. One primary issue is how to handle the continuous cache misses in the new routing path during a handoff process. To resolve the cache missing issue and reduce its handoff delay, different prefetching approaches have been proposed. However, the pre-installed rules in current prefetching approaches will incur extra delay in the core network, which cannot be ignored. In this paper, based on network calculus theory, we develop an analytical handoff delay performance model for different prefetching approaches in SDN- enabled mobile networks. The derived delay bounds of prefetching approach in different network environments are validated by simulations. We show that the presented network- calculus-based delay analysis methodology can be an effective and interesting analytical approach for evaluating ultra low delay communications systems in 5G wireless.

In this paper, we present a systemic approach to provide deterministic delay guarantee for dynamic service chaining in software defined networking (SDN). The delay performance of service chaining in SDN is affected by signaling message exchange in control plane and packet transmissions in data plane, respectively. First, we develop an analytical method to characterize the delay performance of control plane when handling traffic with different priorities according to network calculus (NC) and queuing theory. Second, taking into account the estimated delay in control plane, we propose a novel service traversal mechanism to calculate the optimal traversal path for the service chain. We demonstrate that NC delay analysis can provide deterministic quality of service (QoS)-guaranteed service chaining for any specified delay requirements, whereas theoretical queueing delay analysis can only provide statistical QoS guarantee. In summary, the proposed NC delay analysis can help to understand the network design for a future delay sensitive Internet in which deterministic latency must be guaranteed.

Software defined network (SDN) is an emerging network architecture which offloads the control logic of the network from the underlying forwarding devices to a centralized controller. This centralized control intelligence software defines the behavior of the network. However, the programmability and centralization of the SDN architecture introduce potential security concerns. In this paper, we first investigate the threats of denial of service (DoS) attacks on the SDN control channel. Then, we evaluate the impact of DoS by simulating a DoS attack against the network controller. Our results show that it is possible to exhaust the controller resources in the absence of an authentication mechanism. Finally, we propose a lightweight information hiding authentication mechanism to prevent DoS attacks in the SDN control channel.

In recent years, the wireless local area networks (WLAN) access points (APs) are being deployed rapidly in the offices and the campuses to satisfy the quality of service (QoS) requirements such as network bandwidth. However, many empirical studies show that a large fraction of idle WLAN resources result in the significant energy losses. To reduce the energy consumption without violating the QoS requirement, we propose a QoS-aware AP energy saving mechanism using software defined network (SDN). We leverage the capability of the SDN controller to dynamically monitor the network condition information to estimate the network bandwidth requirement of the devices and then manage the network forwarding to provide seamless handover. Our experimental results show that the proposed scheme can effectively reduce the number of power-on WLAN APs while still providing QoS guarantee, such as network bandwidth and handover cost.

Network coding is an important cloud storage technique, which can recover data with small repair bandwidth and high reliability compared to the existing erasure coding and replication methods. However, regardless of which data recovery technique is used, the repaired data in a geographically distributed cloud storage system are easy to be eavesdropped at the transmission link between the local datacenter and its remote backup site. This kind of network security issue is called link eavesdropping in this paper. For a network coded cloud storage system, we propose a systematic design methodology to determine the important data recovery system parameters for any specified security level. Through analysis, we present the performance curves to relate the remote repair bandwidth and the number of coded data fragments. Consequently, all the important system parameters of a network coded data recovery system, including the number of storage nodes and the link capacity between the datacenter and the backup site, can be precisely designed for satisfying different security level requirements.

In this paper we propose a traffic-aware networking technique by using software-defined networking (SDN) to precisely and promptly identify video streaming packets. We performed experiments on our SDN testbed. Compared with the existing deep packet inspection (DPI) method, the proposed SDN-enabled traffic-aware packet routing technique can reduce the latency by 75% and increase the success rate for traffic identification up to 138%.

In this paper, we propose a cloud-assisted network coded packet retransmission approach to reduce the number of packet retransmission in wireless multicasting. It is shown that the efficiency of packet retransmission can be significantly improved if we include network topology information (e.g., Network connectivity) during the encoding process of network coding. We leverages the capability of software-defined networking (SDN) to dynamically monitor and control the entire network and design a network topology based network coded packet retransmission (NTNCPR) mechanism. The proposed NTNCPR mechanism can easily calculate the good packet combination based on network topology information.

To relieve the heavy loading caused by burst machine-to-machine (M2M) traffic and also satisfy various quality of service (QoS) requirements, load balancing techniques are often introduced in M2M networks. In recent years, software-defined networking (SDN) has shown the possibility of improving load balancing technique. In this paper, we propose traffic-aware load balancing mechanism for M2M networks using SDN. The proposed mechanism can satisfy different QoS requirements of M2M traffic by instant traffic identification and dynamic traffic rerouting, which leverage the capability of SDN to dynamically monitor and control the entire network.

For security concerns, a security traversal service can route data flows through a sequences of security devices (middleboxes). In this paper, we identify the problem of delay guarantee in security traversal and propose a scheme to dynamically change the security traversal path. To provide deterministic delay guarantee with minimum virtual machine (VM) and transmission cost, we model this security traversal path determination as a constrained shortest path problem (CSP) and propose an optimal security traversal with middlebox addition (OSTMA) mechanism. Besides, we implement the proposed OSTMA mechanism in an OpenFlow network by designing a centralized security traversal controller to dynamically monitor the network condition information and reconfigure the security traversal path. Our experimental results show that the proposed dynamic security traversal scheme can still achieve delay requirements for network topology changes and burst traffic.

We consider the cloud storage systems with data stored in two geographically different datacenters for remote backup. In such system, inter-data center communication is established for data repair when storage nodes fail in the data center. Since the repairing data are transmitted over the Internet, the communication between the datacenters can become susceptible to eavesdropping. This problem is especially crucial in network coding-based distributed storage systems because more repair bandwidth and repair links are required, compared to conventional replication. In this paper, we show that remote repair bandwidth can be reduced by increasing storage per node and derive the tradeoff curves between remote repair bandwidth and storage. Moreover, we show that there exist another tradeoff for storage cost and reliability for different amount of remote and local storage nodes.

Communication networks such as cellular phone networks are quite likely to be severely damaged during a large-scale of disaster, making SOS message dissemination to rescue authorities extremely difficult. In this paper, we first propose a FM radio-based emergency communication service through the integration of FM transmitters into smartphones. FM radio provides a number of advantages such as longer propagation length and less susceptible to obstacles, making it suitable for broadcasting SOS messages. Besides, we design Morse code-based SOS message dissemination using FM transmitters (MCSOS-FM) and its corresponding communication procedure for the emergency communication service in order to improve communication range, victim localization and evacuation route planning. The combination of FM radio and Morse code is completely compatible with the current radio system equipped by rescue workers, and thus increases the possibilities of successful receiving and recognizing SOS messages. Our experiment result based on smartphone implementation shows that the proposed emergency communication service is cost effective and energy efficient with relatively large communication range.

In this paper, we propose a sensors-assisted rescue service architecture to integrate rescue schemes for different purposes, including disaster prediction, evacuation planning, and emergency broadcast. In the proposed architecture, multiple-sensed mobile devices are designed to provide a personalized situational awareness, thereby further enhancing the flexibility and efficiency of rescue services. Reliability and scalability of rescue services are improved by leveraging the dynamical resource provision of cloud computing. The proposed rescue service architecture is implemented to show the advantages of power efficiency and scalability of the proposed rescue service architecture.

Smart phones with touch screens have become very popular and have changed our behaviors of using handsets. However, using touch screens is not safe for mobile phone users especially when they are driving cars. Thus, many applications using smart phones cannot be initiated because users who are driving the cars cannot easily touch the small icons on the screens of smart phones. To overcome this issue, we propose a proximity sensors based "no-touch" mechanism for smart phones by applying proximity sensors to initiate mobile applications without the need of touching the screen. We will discuss how to implement the proximity-sensors driven "no-touch" mechanism in Android platform and investigate its performance issues regarding detection accuracy and power consumption. Speech-oriented applications using the proposed "no-touch" mechanism on Android is also demonstrated in this paper.