Quantum-inspired neural networks have demonstrated strong potential in modeling non-classical phenomena in cognitive tasks, particularly in multimodal sentiment analysis, marking a significant advancement over traditional models. However, existing multimodal quantum-inspired neural networks fall short in fully modeling the multimodal density matrix, typically relying on simplistic neural mappings to represent quantum entanglement. This lack of explicit physical constraints, particularly those governing open quantum system dynamics, limits both the interpretability and performance. To address this limitation, we propose a novel framework grounded in quantum stochastic dynamics, introducing two quantum-inspired neural networks, which model the evolution of multimodal data as Markovian and non-Markovian open quantum systems, respectively. This approach enables the simulation of quantum system evolution to capture rich non-classical interactions between modalities. The resulting entangled multimodal density matrix is then measured through quantum projections to extract high-level features for downstream sentiment analysis and sarcasm detection. Extensive experiments on benchmark bimodal and trimodal datasets demonstrate that our models consistently outperform state-of-the-art traditional baselines, large-scale language models and quantum-inspired neural networks. Ablation studies confirm the critical role of quantum stochastic dynamics in performance gains. Furthermore, we enhance the interpretability by tracking the evolution of the density matrix using von-Neumann entanglement entropy as a quantitative metric, providing deeper insight into the internal mechanisms of the model.

Multi-signature is a typical digital signature for signing one message by multiple signers. Its advantage is that the length of the signature is independent of the number of signers, even though multiple users sign the same message. It can therefore serve as a fundamental building block in many secure multiple computation scenarios, e.g., signing a transaction in blockchain applications, signing a routing message in routing discovery protocols, and so on. Nonetheless, most of the existing multi-signature schemes have been still based on traditional signatures over the hardness of number theory problems such as integer factoring or discrete logarithm, which may be insecure in future quantum computing environment. Besides, they impose the cost of public key infrastructure due to the authentication of public keys. Instead, identity-based (ID-based) signature can avoid such overhead due to the management of public keys by using a public identity as a public key. Therefore, in this paper we propose a broadcast multi-signature scheme that is ID-based and lattice-based (i.e., with the hardness assumption on solving lattice problems). Our scheme has the advantages from not only ID-based signatures but also lattice-based signatures. Moreover, the scheme is efficient as the signature length is short, which is suitable for length sensitive applications, e.g., signing transactions in a blockchain.

The unmanned aerial vehicle (UAV) resupply mode offers a viable alternative to parcel delivery by replenishing ground vehicles at intermediate open-space supply points. Usually, orders are treated as equally important with no guarantees of on-time delivery. In this paper, we propose a priority-based delivery framework to efficiently manage real-time delivery demands with varying priorities. Specifically, this framework integrates a fleet of UAVs for resupply and trucks for final delivery. By considering the inherent priority of orders and their elapsed waiting time, a dynamic priority discipline is introduced and customized to different order contexts. This approach ensures that urgent orders are prioritized to ensure timely fulfillment within their deadlines, while low-prioritized orders can still be completed without excessive waiting. Simulations using a real-world city map of Helsinki, along with mobility features of both trucks and UAVs, show significant performance gains over baseline schemes in terms of on-time delivery rate, average delivery time, and waiting fairness among all orders.

Real-time kinematic (RTK) positioning is a widely used technique that improves positioning accuracy to the centimeter level. In RTK systems, users can utilize the correction data provided by a reference station (RS) to offset their location errors. However, due to the limited deployment and high cost of RS, some places are not covered by physical RS (within 30 kilometers). Therefore, a virtual reference station (VRS) RTK service is provided, where a server generates VRS for users and provides seamless cloud-based network RTK corrections without the need of a real RS. Despite the benefits of VRS services, it poses a risk to user location privacy, as users need to continuously report their standalone calculated positions to the server to obtain the service. In this paper, we propose a privacy-preserving approach for VRS services by multicasting correction data over mobile networks, where joined users can receive valid correction data from the base station in either inactive or connected mode. Experiment results show that the network delay is less than 40ms, throughput is about 16kbps, and the RTK positioning error is 4cm. Other qualitative analyses (such as service continuity and stability) also prove the feasibility and effectiveness of the proposed method.

With the advancement of Internet of Things (IoT) applications, it is essential to utilize an IoT platform to facilitate data exchange and application deployment. Existing platforms are typically either data-cloud-based or data-centralized, relying on servers as repeaters to exchange data. However, these architectures often face limitations related to triangle routing, network bottlenecks, and data scalability challenges, particularly in AIoT (Artificial Intelligence of Things) applications that require the fusion of numerous high-volume data streams. These challenges can be significantly mitigated through data-decentralized direct sender-to-receiver exchanges with a remote 'Agent', which is responsible for connectivity management. This work presents a prototype data-decentralized AIoT platform (DAIoTtalk) featuring peer-to-peer communications empowered by customized gRPC remote procedure calls based on the publish-subscribe (pub-sub) paradigm. An extension of IoTtalk, DAIoTtalk ensures device management with more adaptable node networking and offers a test bed for low-code development with decentralized communications. We demonstrate through extensive experiments that our design achieves at least 3 times more efficiency than a data-centralized approach. We also develop a case study to showcase the flexibility of our platform.

Global navigation satellite systems (GNSS) error source analysis is crucial for identifying factors that affect the accuracy of positioning, navigation, and timing services (PNT). Detecting and correcting these factors is essential for enhancing overall service accuracy. Traditional methods primarily focus on surface-level receiver output data, which may overlook underlying factors. Additionally, analyzing daily generated data is expensive and requires advanced proficiency. This research uses a novel temporal-spatial analysis approach to analyze GNSS error sources with artificial intelligence (AI) model support. We develop a noise segments dataset categorized into six types, with a particular focus on ionospheric disclosure, a deeper-level receiver data calculating PNT result. By applying clustering combined with a z-score normalization filter (ZFilter), we identify highly consistent noise segments in daily data, which aids in understanding potential causes. We then employ a multi-model deep learning approach to classify the noise segments, as opposed to relying on a single baseline model. Additionally, we experiment with semi-supervised learning through pseudo-labeling to improve classification performance. Our experiments show that our classifier achieves approximately 84% accuracy in identifying the noise segments.

Reputation updating plays a vital role in cloud-supported vehicular networks, ensuring the continuous freshness of trustworthiness. However, the existing solutions suffer from insufficient privacy and security, as well as impose significant computation and communication overheads on resource-constrained vehicles. In addition, they require vehicles to pre-load numerous keys and reputation certificates, complicating certificate management along with key escrow and revocation issues. Thus, in this paper, we introduce an innovative Privacy-preserving and Blockchain-assisted Reputation Updating (PBRU) scheme with malicious detection, for cloud-supported vehicular networks. Specifically, based on the improved exponential ElGamal variant, the reputation feedback generation and verification process avoids time-consuming homomorphic exponential and bilinear pairing operations, such that computation and communication overheads of vehicles are significantly reduced by 87.42% and 43.32%, respectively. Besides, the PBRU scheme reconstructs the key derivation algorithm and records reputation certificates on the blockchain, eliminating the need for pre-loading keys and certificates on vehicles while enabling traceability. Moreover, the PBRU scheme is capable of detecting duplicate malicious feedbacks by utilizing Bloom filter. Furthermore, theoretical proof and analysis present that the PBRU scheme satisfies more security requirements than the state-of-the-art schemes. Finally, the comprehensive simulation evaluation demonstrates the effectivity and practicality of our PBRU scheme.

Thanks to mobility and large coverage, 6G mobile networks introduce satellites and unmanned aerial vehicles as aerial base stations (ABS) in the 6G era. Instead of using a wired backhaul in 5G and its predecessor, an ABS leverages a wireless channel to a core network (CN). However, such a wireless channel design introduces new security challenges. In this paper, we present that passive attackers could sniff the ABS-CN wireless channel and identify what users are doing based on deep learning methods. We collect GTP protocol data on our testbed and use convolutional neural networks to classify 5 types of encrypted App traffic, like IG and TikTok. Experiment results proved the effectiveness of the proposed method, revealing the confidential data leakage problem on the 6G wireless ABS-CN channel.

Blockchain has been envisioned as an anonymous cryptocurrency framework and can be applied in various applications such as e-payment, share economics, and distributed ledger. Although an account is anonymous, privacy in terms of consumption behaviors still imposes leakage risks. For example, an adversary may infer the consumption capability related to an account further by analyzing the history of consumption records. It is thus of critical importance to design a solution to preserve the anonymity of both transaction amount and transaction peer’s identity, which is changeable because the amount must be still authenticated in anonymity. In this paper, we propose a scheme by using Pedersen commitment to anonymize the transaction amount, and together using ring signature to conceal the transaction peer’s identity while maintaining authentication. Especially, we further improve the security of anonymous authentication enabled by ring signature by introducing accountability, which can defend against double-spending attacks by penalization and empower auditability. The extensive performance and security analysis justify the applicability of the proposed scheme.

In 5G, network functions can be scaled out/in dynamically to adjust the capacity for network slices. The scale-out/-in procedure, namely autoscaling, enhances performance by scaling out instances and reduces operational costs by scaling in instances. However, the autoscaling problems in 5G networks are different from those in traditional cloud computing. The 5G network functions must be considered the simultaneous deployment of multiple instances; moreover, the deployment of 5G network functions is more frequent than that of traditional cloud computing. Both the number and timing of deployment will substantially affect the cost-effectiveness of the system. In this paper, we first identify the autoscaling issues specifically based on the 3GPP standards. We develop a low-complexity analytical queuing model to formulate the problem and quantify a set of performance metrics with closed-form solutions. The proposed analytical model and closed-form solutions are cross-validated by extensive simulations. The analytical model offers design insights and theoretical guidelines, helping us study the effectiveness of reservations. We proposed a dynamic block-setup reservation algorithm (DBRA) to find the optimal reserved number and threshold value of network slices. Therefore, mobile operators can balance the system's cost-effectiveness without large-scaled testing and real deployment, saving cost on time and money.

In 5G, multi-access edge computing enables the applications to be offloaded to near-end edge servers for faster response. According to the 3GPP standards, users in 5G are separated into many types, e.g., vehicles, AR/VR, IoT devices, etc. Specifically, the high-priority traffic can preempt edge resources to guarantee the service quality. However, even if a traffic is transmitted with low priority, its latency requirement in 5G is much lower than that in 4G. Too strict latency requirement and priority-based service make resource configuration difficult on the edge side. Therefore, we propose the edge-cloud offloading mechanism, in which each edge server can offload tasks to back-end cloud server to ensure service quality of both high- and low-priority traffic. In this paper, we establish a priority-based queuing system to model the edge-cloud offloading behaviors. Based on the formulation of our system model, we propose Knapsack Potential Game (KPG) to derive an optimal offloading ratio for each edge server to balance the cost-effectiveness of the overall system. We demonstrate that KPG has low computational complexity and outperforms two baseline algorithms. The results indicate that KPG’s performance is optimal and provides a theoretical guideline to operators while designing their edge-cloud offloading strategies without large-scale implementation.

Learning discriminative and invariant feature representation is the key to visual image categorization. In this article, we propose a novel invariant deep compressible covariance pooling (IDCCP) to solve nuisance variations in aerial scene categorization. We consider transforming the input image according to a finite transformation group that consists of multiple confounding orthogonal matrices, such as the D4 group. Then, we adopt a Siamese-style network to transfer the group structure to the representation space, where we can derive a trivial representation that is invariant under the group action. The linear classifier trained with trivial representation will also be possessed with invariance. To further improve the discriminative power of representation, we extend the representation to the tensor space while imposing orthogonal constraints on the transformation matrix to effectively reduce feature dimensions. We conduct extensive experiments on the publicly released aerial scene image data sets and demonstrate the superiority of this method compared with state-of-the-art methods. In particular, with using ResNet architecture, our IDCCP model can reduce the dimension of the tensor representation by about 98% without sacrificing accuracy (i.e., < 0.5%).

Deep learning has recently been intensively studied in the context of image compressive sensing (CS) to discover and represent complicated image structures. These approaches, however, either suffer from nonflexibility for an arbitrary sampling ratio or lack an explicit deep-learned regularization term. This paper aims to solve the CS reconstruction problem by combining the deep-learned regularization term and proximal operator. We first introduce a regularization term using a carefully designed residual-regressive net, which can measure the distance between a corrupted image and a clean image set and accurately identify to which subspace the corrupted image belongs. We then address a proximal operator with a tailored dilated residual channel attention net, which enables the learned proximal operator to map the distorted image into the clean image set. We adopt an adaptive proximal selection strategy to embed the network into the loop of the CS image reconstruction algorithm. Moreover, a self-ensemble strategy is presented to improve CS recovery performance. We further utilize state evolution to analyze the effectiveness of the designed networks. Extensive experiments also demonstrate that our method can yield superior accurate reconstruction (PSNR gain over 1 dB) compared to other competing approaches while achieving the current state-of-the-art image CS reconstruction performance. The test code is available at https://github.com/zjut-gwl/CSDRCANet.

The emergence of voice over LTE enables voice traffic transmissions over 4G packet-switched networks. Since voice traffic is characterized by its small payload and frequent transmissions, the corresponding control channel overhead would be high. Semi-persistent scheduling (SPS) is hence proposed in LTE-A to reduce such overhead. However, as wireless channels typically fluctuate, tremendous retransmissions due to poor channel conditions, which are still scheduled dynamically, would lead to a large overhead. To reduce the control message overhead caused by SPS retransmissions, we propose a new SPS retransmission protocol. Different from traditional SPS, which removes the downlink control indicators (DCI) directly, we compress some key fields of all retransmissions' DCIs in the same subframe as a fixed-length hint. Thus, the base station does not need to send this information to different users individually but just announces the hint as a broadcast message. In this way, we reduce the signaling overhead and at the same time, preserve the flexibility of dynamic scheduling. Our simulation results show that, by enabling DCI compression, our design improves signaling efficiency by 2.16×, and the spectral utilization can be increased by up to 60%.

The surge of M2M devices imposes new challenges for the current cellular network architecture, especially in radio access networks. One of the key issues is that the M2M traffic, characterized by small data and massive connection requests, makes significant collisions and congestion during network access via the random access (RA) procedure. To resolve this problem, in this paper, we propose a paging cycle-based protocol to facilitate the random access procedure in LTE-A. The high-level idea of our design is to leverage a UE's paging cycle as a hint to preassign RA preambles so that UEs can avoid preamble collisions at the first place. Our rpHint has two modes: (1) collision-free paging, which completely prevents cross-collision between paged user equipment (UEs) and random access UEs, and (2) collision-avoidance paging, which alleviates cross-collision. Moreover, we formulate a mathematical model to derive the optimal paging ratio that maximizes the expected number of successful UEs. This analysis also allows us to adapt dynamically to the better one between the two modes. We show via extensive simulations that our design increases the number of successful UEs in an RA procedure by more than 3× as compared to the legacy RA scheme of the LTE.

The emergence of Network Function Virtualization (NFV) enables flexible and agile service function chaining in a Software Defined Network (SDN). While this virtualization technology efficiently offers customization capability, it however comes with a cost of consuming precious TCAM resources. Due to this, the number of service chains that an SDN can support is limited by the flowtable size of a switch. To break this limitation, this paper presents CRT-Chain, a service chain forwarding protocol that requires only constant flowtable entries, regardless of the number of service chain requests. The core of CRT-Chain is an encoding mechanism that leverages Chinese Remainder Theorem (CRT) to compress the forwarding information into small labels. A switch does not need to insert forwarding rules for every service chain request, but only needs to conduct very simple modular arithmetic to extract the forwarding rules directly from CRT-Chain's labels attached in the header. We further incorporate prime reuse and path segmentation in CRT-Chain to reduce the header size and, hence, save bandwidth consumption. Our evaluation results show that, when a chain consists of no more than 5 functions, CRT-Chain actually generates a header smaller than the legacy 32-bit header defined in IETF. By enabling prime reuse and segmentation, CRT-Chain further reduces the total signaling overhead to a level lower than the conventional scheme, showing that CRT-Chain not only enables scalable flowtable-free chaining but also improves network efficiency.

With the increasing popularity of machine-type communication (MTC) devices, several new challenges are encountered by the legacy long term evolution (LTE) system. One critical issue is that a massive number of MTC devices trying to conduct random access procedures may cause significant collisions and long delays. In this work, we present a new random access mechanism by splitting the contention-based preambles in LTE into two logically disjoint parts, one for the user equipment (UE) being paged and the other for the UEs not being paged. Since the IDs of paged UEs are known by the base station, a novel hash-based random access, which we call hint, is possible. The main idea is to pre-allocate preambles to paged UEs in a contention-free manner and confines non-paged UEs to contend in a separate region. We further build a mathematical model to find the optimal ratio of pre-allocated preambles. Extensive simulations are conducted to validate our results.

Cloud computing paradigm is becoming very popular these days. However, it does not include wireless sensors and mobile phones which are needed to enable new emerging applications such as remote home medical monitoring. Therefore, a combined Cloud-Internet of Things (IoT) paradigm provides scalable on-demand data storage and resilient computation power at the cloud side as well as anytime, anywhere health data monitoring at the IoT side. As both the privacy of personal medical data and flexible data access should be provided,attackers exploit diverse social engineering and technology attacks ways, access to personal privacy information stored in the home medical monitoring cloud, with more and more social engineering attacks.Therefore, the data in the Cloud are always encrypted and access control must be operated upon encrypted data together with being fine-grained to support diverse accessibility. Since a plain combination of encryption before access control is not robust and flexible, we propose a scheme referred to as RoFa, with tailored design. The scheme is introduced in a step-by-step manner. The basic scheme (BaS) makes use of cipher-policy attributes based encryption to empower robustness and flexibility. We further propose an advanced scheme (AdS) to improve the computation efficiency by taking the advantages of proxy-reencryption. AdS can greatly decrease the computation overhead on hospital servers due to operation migration. We finally propose an enhanced scheme (EnS) to protect integrity by using aggregate signature. RoFa describes a general framework to solve the secure requirements, and leaves the flexibility of concrete constructions intentionally. We finally compare the robustness and the flexibility of the proposed schemes by performance analysis.

We can find various Internet of Things (IoT) products available in the market to accommodate users needs. Current IoT applications, however, built delicately for their usage with their own sensor devices and Apps. This limitation thus prevents users from handling flexible situations. In this paper, we propose iToy, an LEGO-like solution to integrate various off-the-shelf sensors for different IoT applications. In iToy, the off-the-shelf sensors are like LEGO plastic bricks, which can be assembled and connected to construct IoT applications. Any sensors constructed in an IoT application can be taken apart again and then used to make other IoT applications. We demonstrate through proof-of-concept prototype that iToy is user-friendly and suitable for forming different small scale IoT applications.

The paradigm shift toward SDN has exhibited the following trends: (1) relying on a centralized and more powerful controller to make intelligent decisions, and (2) allowing a set of relatively dumb switches to route packets. Therefore, efficiently looking up the flowtables in forwarding switches to guarantee low latency becomes a critical issue. In this paper, following the similar paradigm, we propose a new routing scheme called KeySet which is flowtable-free and enables constant-time switching at the forwarding switches. Instead of looking up long flowtables, KeySet relies on a residual system to quickly calculate routing paths. A switch only needs to do simple modular arithmetics to obtain a packet's forwarding output port. Moreover, KeySet has a nice fault- tolerant capability because in many cases the controller does not need to update flowtables at switches when a failure occurs. We validate KeySet through extensive simulations by using general as well as Facebook fat-tree topologies. The results show that the KeySet outperforms the KeyFlow scheme [1] by at least 25% in terms of the length of the forwarding label. Moreover, we show that KeySet is very efficient when applied to fat-trees.

Massive Machine Type Communication (mMTC) has attracted increasing attention due to the explosive growth of IoT devices. Random Access (RA) for a large number of mMTC devices is especially difficult since the high signaling overhead between User Equipments (UEs) and an eNB may overwhelm the available spectrum resources. To address this issue, we propose “respond by hint” (r-Hint), an ID-free handshaking protocol for contention-based RA in mMTC. The core idea of r-Hint is to avoid sequentially notifying contending UEs of their IDs by broadcasting a hint in the RA Response (RAR). To do so, we exploit the concept of prime factorization and hashing to encode the hint such that UEs can extract their required information accordingly. Our simulation results show that r-Hint reduces the RAR message size by 20%-40%. Such reduction can be translated to around 50% improvement of spectrum efficiency in LTE-M.

IoT (Internet of Things) has attracted a lot of attention recently. IoT devices need to report their data or status to base stations at various frequencies. The IoT communications observed by a base station normally exhibit the following characteristics: (1) massively connected, (2) lightly loaded per packet, and (3) periodical or at least mostly predictable. The current design principals of communication networks, when applied to IoT scenarios, however, do not fit well to these requirements. When a large number of devices contend to send small packets, the signaling overhead is not cost-effective. To address this problem, our previous work [1] proposes the Hint protocol, which is slot-based and schedule- oriented for uploading IoT devices' data. In this work, we extend [1] to support data transmissions for multiple resource blocks. We assume that the uplink payloads from IoT devices are small, each taking very few slots (or resource blocks), but devices are massive. The main idea is to "encode" information in a tiny broadcast that allows each device to "decode" its transmission slots, thus significantly reducing transmission overheads and contention overheads. Our simulation results verify that the protocol can significantly increase channel utilization compared with traditional schemes.

In cloud computing paradigm, virtual resource autoscaling approaches have been intensively studied recent years. Those approaches dynamically scale in/out virtual resources to adjust system performance for saving operation cost. However, designing the autoscaling algorithm for desired performance with limited budget, while considering the existing capacity of legacy network equipment, is not a trivial task. In this paper, we propose a Deadline and Budget Constrained Autoscaling (DBCA) algorithm for addressing the budget-performance tradeoff. We develop an analytical model to quantify the tradeoff and cross-validate the model by extensive simulations. The results show that the DBCA can significantly improve system performance given the budget upper-bound. In addition, the model provides a quick way to evaluate the budget-performance tradeoff and system design without wide deployment, saving on cost and time.

Collecting data from a tremendous amount of Internet-of-Things (IoT) devices for next generation networks is a big challenge. A large number of devices may lead to severe congestion in Radio Access Network (RAN) and Core Network (CN). 3GPP has specified several mechanisms to handle the congestion caused by massive amounts of devices. However, detailed settings and strategies of them are not defined in the standards and are left for operators. In this paper, we propose two congestion control algorithms which efficiently reduce the congestion. Simulation results demonstrate that the proposed algorithms can achieve 20~40% improvement regarding accept ratio, overload degree and waiting time compared with those in LTE-A.

Network Function Virtualization (NFV) enables mobile operators to virtualize their network entities as Virtualized Network Functions (VNFs), offering fine-grained on-demand network capabilities. VNFs can be dynamically scale-in/out to meet the performance desire and other dynamic behaviors. However, designing the auto-scaling algorithm for desired characteristics with low operation cost and low latency, while considering the existing capacity of legacy network equipment, is not a trivial task. In this paper, we propose a VNF Dynamic Auto Scaling Algorithm (DASA) considering the tradeoff between performance and operation cost. We develop an analytical model to quantify the tradeoff and validate the analysis through extensive simulations. The results show that the DASA can significantly reduce operation cost given the latency upper-bound. Moreover, the models provide a quick way to evaluate the cost- performance tradeoff and system design without wide deployment, which can save cost and time.

LTE femtocells have been commonly deployed by network operators to increase network capacity and offload mobile data traffic from macrocells. A User Equipment (UE) camped on femtocells has the benefits of higher transmission rate and longer battery life due to its proximity to the base stations. However, various user mobility behaviors may incur frequent signaling overhead and degrade femtocell offloading capability. To efficiently offload mobile data traffic, we propose a Threshold Offloading (TO) algorithm considering the trade-off between network signaling overhead and femtocell offloading capability. In this paper, we develop an analytical model to quantify the trade-off and validate the analysis through extensive simulations. The results show that the TO algorithm can significantly reduce signaling overhead at minor cost of femtocell offloading capability. Moreover, this work offers network operators guidelines to set offloading threshold in accordance with their management policies in a systematical way.

3GPP introduced the key management mechanism (KMM) in evolved multimedia broadcast/multicast service (eMBMS) to provide forward security and backward security for multicast contents. In this paper, we point out that KMM may lead to frequent rekeying and re-authentication issues due to eMBMS's characteristics: 1) massive group members; 2) dynamic group topology; and 3) unexpected wireless disconnections. Such issues expose extra load for both user equipment (UE) terminals and mobile operators. It seems prolonging the rekeying interval is an intuitive solution to minimizing the impact of the issues. However, a long rekeying interval is not considered the best operational solution due to revenue loss of content providers. This paper quantifies the tradeoff between the load of the UEs and the operators as well as the revenue loss of the content providers. Moreover, we emphasize how essential this rekeying interval has impacts on the problems. Using our proposed tradeoff model, the operators can specify a suitable rekeying interval to best balance the interest between the above three parties. The tradeoff model is validated by extensive simulations and is demonstrated to be an effective approach for the tradeoff analysis and optimization on eMBMS.

E-healthcare games are a new patient care tool for providing fun, on-line monitoring enabled lifestyle, and a way for redefining traditional doctor-patient relationship. In this work, we demonstrate the use of intelligent sensors to capture patient behaviors for home healthcare. We design a new e-healthcare game user interface, iCube, for monitoring and recording patient activities. The iCube is a 3D game which has six display panels on the surfaces of a 3D cube embedded with a zigbee interface and some sensors for behavior monitoring. To prove its concept, we implement a Spot the Difference game. When a player is playing the game, the iCube can capture the player's hand shaking, rolling response, and visual capability. Those collected data are then sent to a database for spotting the progress/difference of the player over a long period time.

Unattended Wireless Sensor Networks (UWSNs) are characterized by long periods of disconnected operation and fixed or irregular intervals between sink visits. The absence of an online trusted third party implies that existing WSN trust management schemes are not applicable to UWSNs. In this paper, we propose a trust management scheme for UWSNs to provide efficient and robust trust data storage and trust generation. For trust data storage, we employ a geographic hash table to identify storage nodes and to significantly decrease storage cost. We use subjective logic based consensus techniques to mitigate trust fluctuations caused by environmental factors. We exploit a set of trust similarity functions to detect trust outliers and to sustain trust pollution attacks. We demonstrate, through extensive analyses and simulations, that the proposed scheme is efficient, robust and scalable.

Unattended Wireless Sensor Networks (UWSNs) are characterized by long periods of disconnected operation and fixed or irregular intervals between visits by the sink. The absence of an online trusted third party, i.e., an on-site sink, makes existing trust management schemes used in legacy wireless sensor networks not applicable to UWSNs directly. In this paper, we propose a trust management scheme for UWSNs to provide efficient, robust and scalable trust data storage. For trust data storage, we employ geographic hash table to efficiently identify data storage nodes and to significantly reduce storage cost. We demonstrate, through detailed analyses and extensive simulations, that the proposed scheme is efficient, robust, and scalable.

Body Area Network (BAN) consists of various types of small physiological sensors, transmission modules and low computational components and can thus form an E-health solution for continuous all-day and any-place health monitoring. To protect confidentiality of collected data, a shared group key is usually deployed in a BAN, and consequently a secure communication group is generated. In this paper, we propose a bi-directional security and collusion resilience key management scheme for BAN, referred to as FoSBaS. Detailed analysis shows that the scheme can provide both forward security and backward security and resist against collusion attacks. Furthermore, the FoSBaS is implemented on a Sun SPOT based sensor network testbed to evaluate its performance. Experimental results show that a group key can be updated within 102.13 ms with 60.22 mJ energy consumption on a 12 node BAN with 28 bits pairwise key.

How to discover a captured node and to resist node capture attack is a challenging task in Wireless Sensor Networks (WSNs). In this paper, we propose a node capture resistance and key refreshing scheme for mobile WSNs which is based on the Chinese remainder theorem. The scheme is able of providing forward secrecy, backward secrecy and collusion resistance for diminishing the effects of capture attacks. By implementing our scheme on a Sun SPOT based sensor network testbed, we demonstrate that the time for updating a new group key varies from 56 ms to 546 ms and the energy consumption is limited to 16.5 - 225 mJ, depending on the length of secret keys and the number of sensors in a group.

Unattended Wireless Sensor Networks (UWSNs) operated in hostile environments face a risk on data security due to the absence of real-time communication between sensors and sinks, which imposes sensors to accumulate data till the next visit of a mobile sink to off-load the data. Thus, how to ensure forward secrecy, backward secrecy and reliability of the accumulated data is a great challenge. For example, if a sensor is compromised, pre-compromise data accumulated in the sensor is exposed to access. In addition, by holding key secrecy of the compromised sensor, attackers also can learn post-compromise data in the sensor. Furthermore, in practical UWSNs, once sensors stop working for accidents due to node crash or battery depletion, all the data accumulated will be lost. To address the challenges, we propose a secure and reliable data distribution scheme in this paper. Detailed analysis shows that our scheme can provide forward secrecy, probabilistic backward secrecy and data reliability. To further improve probabilistic backward secrecy and data reliability, a constrained optimization data distribution scheme is proposed. Detailed analysis and simulation results show the superiority of the proposed scheme in comparison with several previous approaches developed for UWSNs.

In unattended wireless sensor networks, data are stored locally and retrieved on demand. To efficiently transmit the collectorpsilas retrieval results, data are aggregated along being forwarded. The data confidentiality and integrity should be protected at the intermediate nodes. End-to-end encryption or hop-by-hop encryption based schemes are not efficient. Straightforward homomorphic encryption based scheme is not compromise resilient. To achieve all the desires, we propose a scheme - H2S by making use of both homomorphic secret sharing and homomorphic encryption. The security and efficiency of our scheme are justified by extensive analysis.

In In-networking storage Wireless Sensor Networks, sensed data are stored locally for a long term and retrieved on-demand instead of real-time. To maximize data survival, the sensed data are normally distributively stored at multiple nearby nodes. It arises a problem that how to check and grantee data integrity of distributed data storage in the context of resource constraints. In this paper, a technique called Two Granularity Linear Code (TGLC) that consists of Intra-codes and Inter-codes is presented. An efficient and lightweight data integrity check scheme based on TGLC is proposed. Data integrity can be checked by any one who holds short Inter-codes, and the checking credentials is short Intra-codes that is dynamically generated. The proposed scheme is efficient and lightweight with respect to low storage and communication overhead, and yet checking validity is maintained. Our conclusion is justified by extensive analysis.