The widespread movement to integrate computational thinking (CT) and computer science (CS) into K-12 education has led to rapid expansion. However, this growth is challenged by the reality that many in-service teachers asked to teach CS/CT are novices with little formal preparation. Professional development is critical to bridging this gap, yet many programs struggle to connect with teachers' diverse experiences and move beyond theory to classroom practice. This paper presents an experience report from a multi-year one-week summer course serving over 100 middle and high school teachers, the majority of whom were novices in CT. Our work contributes a novel pedagogical model for CT professional development grounded in the principles of Culturally Relevant Pedagogy (CRP). It is structured around two key components within each session: a ''Teachers as Learners'' mini-lecture that connects CT concepts to teachers' own lived experiences, followed immediately by a ''Teachers as Teachers'' hands-on exercise where they adapt K-12 CT activities for their own students. We call it the ''Dual CRP Model''. Pre- and post-course survey data consistently show that teachers enter with low confidence and leave feeling highly prepared to teach CT. Qualitative findings and an analysis of course artifacts reveal that the model's success stems from its use of culturally relevant analogies to make concepts accessible and its bridge from theory to classroom practice, which are key factors in building both conceptual understanding and pedagogical confidence.

Existing congestion control algorithms for MPTCP that care about only long flow transmission aim at the Congestion-Avoidance (CA) phase and they need a long time to reach convergence states. We verified that the exponential growth of congestion window (cwnd) in the uncoupled Slow-Start (SS) leads to not only unfairness to TCP but also buffer overflow due to burst data. Moreover, these algorithms cannot support fair bandwidth sharing among TCP/MPTCP flows before reaching convergence at the bottleneck, which may reduce the transmission efficiency of short flows and even hurts long flows. In this paper, we propose a Throughput Consistency Congestion Control (TCCC) algorithm consisting of Coupled Slow-Start (CSS) and Aggressive Congestion Avoidance (ACA). To prevent packet loss caused by excessive burst data, CSS couples the increment of subflows’ cwnd and reset the ssthresh value to safely move the flows to CA when it achieves expected throughput. Based on CSS, ACA periodically detects path states and allocates the same throughput increment as the best TCP to subflows to achieve fair bandwidth share in CA. Finally, we implement TCCC in both NS3 and real testbed. The results show that TCCC reduces retransmissions, improves transmission efficiency, and maintains better fairness.

Mobile crowdsensing has emerged as a promising paradigm where location-based sensing tasks are outsourced to mobile participants carrying sensor-equipped devices. A critical issue of crowdsensing is to guarantee the sensing coverage by appropriately recruiting participants, which requires participants’ precise locations and thus raises privacy concerns. In this paper, we are motivated to develop a privacy preserving participant recruitment scheme for mobile crowdsensing, which maximizes the spatial coverage of the sensing range while protecting participants’ location privacy against an untrusted crowdsensing platform. Briefly, we propose a utility-assured location obfuscation mechanism operated in a hexagonal grid system, which the participants can follow to locally perturb their locations with personalized privacy demands. Given the obfuscated locations, we efficiently solve a coverage-maximized participant recruitment problem with the budget constraint by using a deterministic rounding algorithm. Considering the existence of biased sensing data incurred by location obfuscation, we further develop a fault-aware crowdsensing framework to improve the robustness of the recruitment strategy, where a constant-approximation algorithm is applied to select participants against any number of unqualified sensing results. Extensive simulations on real-world location datasets and Uber’s geospatial indexing system validate the efficacy of our location obfuscation mechanism and participant recruitment schemes in mobile crowdsensing systems.

The large-scale integration of distributed energy resources has resulted in surgical changes in energy trading systems. Traditional centralized trading systems suffer from high management cost and low efficiency. The recent advance of blockchain technology has enabled the invention of distributed energy trading systems, which can overcome the limitations of centralized trading systems. However, the distributed energy trading systems also bring new security and privacy challenges. For instance, transactions on blockchain are publicly visible which can lead to privacy leakage of trading information. Moreover, user privacy can also be leaked during verification of the aggregated energy trading result. In this paper, we propose a privacy-preserving energy trading scheme based on blockchain to meet the security requirements for distributed energy trading. We adopt a stealth transmission approach based on blockchain to ensure data privacy and break the linkage between consumers and providers in the energy trading process. We also use the non-interactive zero-knowledge proof technology to achieve privacy-preserving and trustworthy trading result verification. Security analysis and evaluation results have demonstrated that the proposed scheme can effectively protect the data privacy for distributed energy trading systems.

Smart grid adopts two-way communication and rich functionalities to gain a positive impact on the sustainability and efficiency of power usage, but on the other hand, also poses serious challenges to customers' privacy. Existing solutions in smart grid usually use cryptographic tools, such as homomorphic encryption, to protect individual privacy, which, however, can only support limited and simple functionalities. Moreover, the resource-constrained smart meters need to perform heavy asymmetric cryptography in these solutions, and thus unnecessarily increases load on smart grid. In this paper, we present a practical and secure SGX-enabled smart grid system, named SecGrid. Our system leverages trusted hardware SGX to ensure that grid utilities can efficiently execute rich functionalities on customers' private data, while guaranteeing their privacy. With our well-devised security protocols in SecGrid, only the smart meters need to perform AES encryption. To validate the superiority of our design, we conduct security analysis and experimentation. Security analysis shows that SecGrid can thwart various attacks from malicious adversaries, and the experimental results show that SecGrid is much faster than the existing privacy-preserving schemes in smart grid.

Mobile crowdsensing is recognized to be a promising paradigm wherein location-based sensing tasks are outsourced to participants carrying mobile devices. A prominent issue of crowdsensing is to guarantee the sensing coverage by appropriately recruiting participating devices, which requires the disclosure of participants' locations and leads to potential location privacy threats. In this paper, we aim to develop a privacy-preserving participant recruiting scheme for mobile crowdsensing, which guarantees the crowdsensing coverage while preserving participants' location differential privacy against a semi-honest crowdsensing aggregator. Briefly, based on the differential private geo-indistinguishability method, we enable candidate participants to locally perturb their location data. With the obfuscated location information, we formulate the crowdsensing coverage optimization as an Integer Program (IP), and develop a 1-(1 - 1/f)f-approximation algorithm, which yields a near-optimal participant recruiting solution. Through extensive simulations, we demonstrate the tradeoff between privacy preservation and crowdsensing utility, and show that satisfactory crowdsensing coverage can be achieved while preserving the participants' differential location privacy.

Information Centric Networking (ICN), a new paradigm of Internet infrastructure, aims to better accommodate users' rapid growing demand for content delivery and optimize bandwidth utilization. Although the in-network cache feature of ICN facilitates the dissemination of content to users, it also poses new challenges on access control for content and network resource. Moreover, it is common that the access privilege of content dynamically change over time. However, existing access control mechanisms in ICN cannot support the publication and distribution of such time-sensitive content. In this paper, we propose a time- sensitive, lightweight, and secure access control mechanism, called TSLS, to solve this problem. We introduce broadcast encryption combined with time tokens for content providers to protect content confidentiality, and only authorized users satisfying the time limitation have capability to decrypt and access the content. Besides, a fast lightweight challenge-response verification is implemented at the edge routers to block unauthorized request from injecting into the network. The responses of authorized users are forwarded to content providers for pre-distribute popular content at in-network caches in advance. Our security analysis shows that TSLS possesses the properties of data confidentiality, unforgeability, anonymity, and DoS/DDoS attacks resistance. Our simulation results indicate that our proposed TSLS is an efficient mechanism with low computation cost and network delay.

Nowadays, multimedia content retrieval has become the major service requirement of the Internet and the traffic of these contents has dominated the IP traffic. To reduce the duplicated traffic and improve the performance of distributing massive volumes of multimedia contents, in-network caching has been proposed recently. However, because in-network content caching can be directly utilized to respond users' requests, multimedia content retrieval is beyond content providers' control and makes it hard for them to implement access control and service accounting. In this paper, we propose an attribute-based accountable access control scheme for multimedia content distribution while making the best of in-network caching, in which content providers can be fully offline. In our scheme, the attribute-based encryption at multimedia content provider side and access policy based authentication at the edge router side jointly ensure the secure access control, which is also efficient in both space and time. Besides, secure service accounting is implemented by letting edge routers collect service credentials generated during users' request process. Through the informal security analysis, we prove the security of our scheme. Simulation results demonstrate that our scheme is efficient with acceptable overhead.

The transportation network company (TNC) services efficiently pair the passengers with the vehicles/drivers through mobile applications, such as Uber, Lyft, Didi, etc. TNC services definitely facilitate the traveling of passengers, while it is equally important to effectively and intelligently schedule the routes of cruising TNC vehicles to improve TNC drivers' revenues. From the TNC drivers' side, the most critical question to address is how to reduce the cruising time, and improve the efficiency/earnings by using their own vehicles to provide TNC services. In this paper, we propose a deep reinforcement learning-based TNC route scheduling approach, which allows the TNC service center to learn about the dynamic TNC service environment and schedule the routes for the vacant TNC vehicles. In particular, we jointly consider multiple factors in the complex TNC environment, such as locations of the TNC vehicles, different time periods during the day, the competition among TNC vehicles, etc., and develop a deep Q-network-based route scheduling algorithm for vacant TNC vehicles based on distributed framework, which makes the server closer to the terminal users and accelerates the training speed. Furthermore, we apply the geo-indistinguishability scheme based on differential privacy to preserve the sensitive location information uploaded by the passengers. We evaluate the proposed algorithm's performance via simulations using open data sets from Didi Chuxing. Through extensive simulations, we show that the proposed scheme is effective in reducing the cruising time of vacant TNC vehicles and improving the earnings of TNC drivers.

Device-free activity recognition (DFAR) is a promising technique that detects the activity of a target by analyzing the influence of its existence on surrounding wireless links. It realizes target sensing without the participation or even awareness of the target. The key question of DFAR is how to characterize the influence of the target on wireless links. Existing works mostly utilize statistical features, such as mean and variance in time-domain, and energy as well as entropy in frequency-domain, to characterize the influenced signals. However, statistical features provide only partial information. This paper explores the method on how to characterize the distribution of the signal as a whole. Specifically, we present a novel coherence histogram, which leverages the spatial structural characteristics to better characterize the distribution of the wireless signal. The coherence histogram captures not only the occurrence probability of received signal strength (RSS) measurements, but also the spatial relationship between adjacent RSS measurements as well. Experimental results show that our coherence histogram-based DFAR system could achieve an accuracy of more than 96%, which significantly outperforms other state-of-the-art DFAR systems remarkably.

Information centric networking (ICN) has been regarded as an ideal architecture for the next-generation network to handle users' increasing demand for content delivery with in-network cache. While making better use of network resources and providing better service delivery, an effective access control mechanism is needed due to the widely disseminated contents. However, in the existing solutions, making cache-enabled routers or content providers authenticate users' requests causes high computation overhead and unnecessary delay. Also, the straight-forward utilization of advanced encryption algorithms makes the system vulnerable to DoS attacks. Besides, privacy protection and service accountability are rarely taken into account in this scenario. In this paper, we propose SEAF, a secure, efficient, and accountable edge-based access control framework for ICN, in which authentication is performed at the network edge to block unauthorized requests at the very beginning. We adopt group signature to achieve anonymous authentication and use hash chain technique to reduce greatly the overhead when users make continuous requests for the same file. At the same time, we provide an efficient revocation method to make our framework more robust. Furthermore, the content providers can affirm the service amount received from the network and extract feedback information from the signatures and hash chains. By formal security analysis and the comparison with related works, we show that SEAF achieves the expected security goals and possesses more useful features. The experimental results also demonstrate that our design is efficient for routers and content providers and bring in only slight delay for users' content retrieval.

The advance in mobile communications has escalated the use of transportation network company (TNC) services by residents. The TNC services efficiently pair the passengers with the vehicles/drivers through mobile applications such as Uber, Lyft, Didi, etc. TNC services definitely facilitate the traveling of passengers, while it is equally important to effectively and intelligently schedule the routes of cruising TNC vehicles to improve TNC drivers' revenues. From the TNC drivers' side, the most critical question to address is how to reduce the cruising time, and improve the efficiency/earnings of using their own vehicles to provide TNC services. In this paper, we propose a deep reinforcement learning based TNC route scheduling approach, which allows the TNC service center to learn about the dynamic TNC service environment and schedule the routes for the vacant TNC vehicles. In particular, we jointly consider multiple factors in the complex TNC environment such as locations of the TNC vehicles, different time periods during the day, the competition among TNC vehicles, etc., and develop a deep Q-network (DQN) based route scheduling algorithm for vacant TNC vehicles. We evaluate the proposed algorithm's performance via simulations using open data sets from Didi Chuxing. Through extensive simulations, we show that the proposed scheme is effective in reducing the cruising time of vacant TNC vehicles and improving the earnings of TNC drivers.

Information Centric Networking (ICN) has been regarded as an ideal architecture for the next-generation network to handle users' increasing demand for content delivery with in-network cache. While making better use of network resources and providing better delivery service, an effective access control mechanism is needed due to wide dissemination of contents. However, in the existing solutions, making cache-enabled routers or content providers authenticate users' requests causes high computation overhead and unnecessary delay. Also, straightforward utilization of advanced encryption algorithms increases the opportunities for DoS attacks. Besides, privacy protection and service accountability are rarely taken into account in this scenario. In this paper, we propose a secure, efficient, and accountable access control framework, called SEAF, for ICN, in which authentication is performed at the network edge to block unauthorized requests at the very beginning. We adopt group signature to achieve anonymous authentication, and use hash chain technique to greatly reduce the overhead when users make continuous requests for the same file. Furthermore, the content providers can affirm the service amount received from the network and extract feedback information from the signatures and hash chains. By formal security analysis and the comparison with related works, we show that SEAF achieves the expected security goals and possesses more useful features. The experimental results also demonstrate that our design is efficient for routers and content providers, and introduces only slight delay for users' content retrieval.

Radio-Frequency Identification (RFID) nowadays has been widely used in a variety of fields such as manufacturing, transportation and healthcare, for identification, tracking, and information collection due to the simplicity and low cost of RFID tags. Existing RFID information collection protocols focus on information collection at a single reader, which aim to minimize the collision among RFID tags and the execution time for information collection. However, none of them consider reader scheduling to deal with the collision between neighboring readers that exists in multi-reader RFID systems. In this paper, we propose two reader scheduling schemes for time-efficient information collection in multi-reader RFID systems. The two proposed scheduling schemes can coordinate multiple readers to collect information simultaneously and ensure information from all tags is successfully collected with reader-reader collision. Extensive simulation results demonstrate the efficiency of the proposed reader scheduling schemes for multi-reader information collection.

Multipath TCP (MPTCP) is designed to offer higher throughput than single-path TCP, and meanwhile MPTCP flow is fair to concurrent TCP flows at the bottleneck. Although the coupled congestion control in current MPTCP can achieve the goals by coupling different subflows, it only focuses on Congestion Avoidance but each subflow still behaves like an independent TCP flow in Slow-Start. However, during Slow-Start, MPTCP is unfair to concurrent TCP flows as it uses more network resources at the shared bottleneck than single-path TCP. Worse still, since the exponential growth of multiple concurrent subflows' congestion windows often results in serious buffer overflow and packet loss at the shared bottleneck, the latency of short flows using MPTCP is often not as good as using TCP. This leads to the fact that MPTCP cannot satisfy the above design goals when handling short flows. To address this issue, we present a Coupled Slow-Start (CSS) Algorithm in this paper. CSS links the exponential growth of subflows' congestion windows to ensure the fairness and reduce the burstiness of Slow- Start. To reduce the packet loss, CSS resets the Slow-Start Threshold (ssthresh) of different subflows for MPTCP to safely move to Congestion Avoidance when it achieves its expected throughput. Our simulation shows that CSS can lower short flows' latency of up to 45% and significantly reduce the packet loss in two typical network environments, meanwhile CSS is TCP-friendly at the shared bottleneck. Simulation results also indicate that CSS can perform at least as well as original MPTCP for the bulk data transfer in common network environments.

Smart grid facilitates reliable and efficient power generation and transmission by integrating information and communication technologies. By collecting users' power demands in advance, the control center (power operator) can adjust the amount of electricity generated to reduce the excess power, which can increase the profit of the power operator. However, on the one hand, user's privacy becomes a critical issue, since it may leak out a user's life habits, which may make user's safety and belongings under threat. On the other hand, the system needs to arm the capability to avoid diverse adversaries' attacks and trace misbehaving users (who request power irresponsibly). In this paper, we propose a privacy-preserving and real-time traceable power request scheme to fulfill the security requirements. We utilize aggregator as a proxy between users and the control center, which verifies the messages and aggregates multiple users' requests together to preserve their privacy. More importantly, this privacy-preserving mechanism has no effect for the control center to whether charge each user, or trace the misbehaving users in real time. The performance analysis shows that our scheme is efficient in terms of computation and storage overhead.

Data access control is a challenging issue in public cloud storage systems. Ciphertext-policy attribute-based encryption (CP-ABE) has been adopted as a promising technique to provide flexible, fine-grained, and secure data access control for cloud storage with honest-but-curious cloud servers. However, in the existing CP-ABE schemes, the single attribute authority must execute the time-consuming user legitimacy verification and secret key distribution, and hence, it results in a single-point performance bottleneck when a CP-ABE scheme is adopted in a large-scale cloud storage system. Users may be stuck in the waiting queue for a long period to obtain their secret keys, thereby resulting in low efficiency of the system. Although multi-authority access control schemes have been proposed, these schemes still cannot overcome the drawbacks of single-point bottleneck and low efficiency, due to the fact that each of the authorities still independently manages a disjoint attribute set. In this paper, we propose a novel heterogeneous framework to remove the problem of single-point performance bottleneck and provide a more efficient access control scheme with an auditing mechanism. Our framework employs multiple attribute authorities to share the load of user legitimacy verification. Meanwhile, in our scheme, a central authority is introduced to generate secret keys for legitimacy verified users. Unlike other multi-authority access control schemes, each of the authorities in our scheme manages the whole attribute set individually. To enhance security, we also propose an auditing mechanism to detect which attribute authority has incorrectly or maliciously performed the legitimacy verification procedure. Analysis shows that our system not only guarantees the security requirements but also makes great performance improvement on key generation.

Cognitive radio networks (CRNs) have been emerging as a promising technique to improve the spectrum efficiency of wireless and mobile networks, which form spectrum clouds to provide services for unlicensed users. As spectrum clouds, the performance of multi-hop CRNs heavily depends on the routing protocol. In this paper, taking the newly proposed Cognitive Capacity Harvesting network as an example, we study the routing problem in multi-hop CRNs and propose a spectrum-aware anypath routing (SAAR) scheme with consideration of both the salient spectrum uncertainty feature of CRNs and the unreliable transmission characteristics of wireless medium. A new cognitive anypath routing metric is designed based on channel and link statistics to accurately estimate and evaluate the quality of an anypath under uncertain spectrum availability. A polynomial-time routing algorithm is also developed to find the best channel and the associated optimal forwarding set and compute the least cost anypath. Extensive simulations show that the proposed protocol SAAR significantly increases packet delivery ratio and reduces end-to-end delay with low communication and computation overhead, which makes it suitable and scalable to be used in multi-hop CRNs.

In cognitive radio networks (CRNs), spectrum database has been well recognized as an effective means to dynamically sharing licensed spectrum among primary users (PUs) and secondary users (SUs). In spectrum database, the protected incumbents (a.k.a. PUs) and the CRs (a.k.a. SUs) are required to register in database their operational specifications such as transmitting power, antenna height, time of operation and etc. so as to provide an up-to-date radio map for public queries and avoid possible interference. However, it poses potentially serious privacy problems especially when governmental and military systems participate in spectrum sharing through spectrum database. Most recent research works in database-driven CRNs, however, only focused on protecting user's location privacy but merely studied preserving PUs' operational specifications. In this paper, we propose a secure and privacy-preserving scheme using hidden policy-assisted attribute-based encryption technique to protect sensitive PUs' operational privacy without affecting database's accessibility and spectrum utilization efficiency. The security and performance analysis demonstrates that our scheme is secure and computationally efficient. Additionally, our policy-assisted scheme is practical and promising because of its consistency with FCC/NTIA's rule in spectrum regulation in database-driven CRNs.

Context awareness plays an important role in many emerging applications, such as mobile computing and smart space. Since FM signal is ubiquitous, it has been recognized as an attractive and promising technique to realize context awareness. When a target is at different locations or performs different activities, it will exert different influence on the FM signal around it. Therefore, it is possible to deduce its location and activity by analysing its influence on the FM signal. However, FM signal is extremely weak and noisy, which makes it a challenging task to achieve high-performance context awareness. In this paper, we propose a new method for improving the performance of an FM-based context-aware system using multidomain features. Specifically, we extract signal features not only from the time domain, but also from the wavelet domain, the frequency domain, and the space domain, and construct robust and discriminative multi-domain features to characterize the FM signal. Furthermore, we also model context awareness as a classification problem and develop a robust iterative sparse representation classification algorithm to efficiently solve this problem. Extensive experiments performed in a 7.2m×10.8m clutter indoor laboratory with one multi-channel FM receiver demonstrate that the proposed schemes could achieve more than 90% accuracy of location estimation and activity recognition when 3 antennas are used.

In cognitive radio networks (CRNs), primary users (PUs) can leverage secondary users (SUs) as cooperative relays to increase their transmission rates, while SUs will in return obtain more spectrum access opportunities, leading to cooperative CRNs (CCRNs). Prior research works in CCRNs mainly focus on providing ubiquitous access and high throughput for users, but have rarely taken energy efficiency into consideration. Besides, most existing works assume that the SUs are passively selected by PUs regardless of SUs' willingness to help, which is obviously not practical. To address energy issue, this paper proposes an energy-efficient cooperative strategy by leveraging temporal and spatial diversity of the primary network. Specifically, SUs with delay-tolerant packets can proactively make the cooperative decisions by jointly considering primary channel availability, channel state information, PUs' traffic load, and their own transmission requirements. We formulate this decision-making problem based on the optimal stopping theory to maximize SUs' energy efficiency. We solve this problem using a dynamic programming approach and derive the optimal cooperative policy. Extensive simulations are then conducted to evaluate the performance of our proposed strategy. The results show significant improvements of SUs' energy efficiency compared with existing cooperative schemes, which demonstrate the benefits of our proposed cooperative strategy in conserving energy for SUs.

To deal with the rapid growth of mobile data traffic and the user interest shift from peer-to-peer communications to content dissemination-based services, such as video streaming, information-centric networking has emerged as a promising architecture and has been increasingly used for wireless and mobile networks. In this paper, we focus on video dissemination in information-centric cognitive radio networks (IC-CRNs) and investigate the use of harvested bands for proactively caching video contents at the locations close to the interested users to improve the performance of video distribution. With consideration of the dynamic and unobservable nature of some parameters, we formulate the allocation of harvested bands as a Markov decision process with hidden and dynamic parameters and transform it into a partially observable Markov decision process and a multi-armed bandit formulation. Based on them, we develop a new spectrum management mechanism, which maximizes the benefit of proactive video caching as well as the efficiency of spectrum utilization in the IC-CRNs. Extensive simulation results demonstrate the significant performance improvement of the proposed scheme for video streaming.

When equipped with multiple antennas, secondary users in cognitive radio networks are able to communicate even when neighboring primary users are active by transmitting in the null space of the communication channel occupied by primary users. In this case, the throughput of a secondary link is limited by the transmission power and the dimension of the null space, i.e., the number of active primary users nearby. Since the number of active primary users is time-varying, the required transmission power to support certain data rate changes from time to time. Thus, secondary users could adapt their transmission to the variation of the primary traffic to improve energy efficiency. In view of that, we develop an energy-efficient traffic scheduling scheme for secondary users equipped with multiple antennas. By formulating the traffic scheduling problem as a Markov decision problem, an energy-efficient transmission scheme is derived from linear programming. The analytical results are verified by simulations and the impacts of various parameters are discussed. The superiority of the derived scheme is also shown by comparing with a randomized scheme.

In cognitive radio networks, primary users (PUs) can leverage secondary users (SUs) as cooperative relays to increase their transmission rates, and SUs will in turn obtain more spectrum access opportunities. While most existing works assume that SUs are passively selected by PUs regardless of SUs' willingness, in this paper, we propose a cooperative strategy for SUs to actively decide whether to cooperate or not. Basically, due to PUs' time-varying traffic demands, it is essential for SUs to firstly observe the channels and then select a specific PU to cooperate with in order to save the energy. In our paper, this decision related problem is formulated based on optimal stopping theory where SUs observe PUs in time sequence and then make decisions whether to stop observation and cooperate right away or wait till next time slot to repeat the same process. We address this problem by using backward induction and derive the energy-efficient strategy for SUs. To validate the feasibility of our proposed scheme, extensive simulations are conducted to show the impact of PUs' traffic demands on SUs' decisions. The results also reveal that the proposed optimal rule outperforms the greedy selection strategy and is thus more energy- efficient to be applied to the cooperative cognitive radio networks.

Machine learning has played an increasing important role in big data systems due to its capability of efficiently discovering valuable knowledge and hidden information. Often times big data such as healthcare systems or financial systems may involve with multiple organizations who may have different privacy policy, and may not explicitly share their data publicly while joint data processing may be a must. Thus, how to share big data among distributed data processing entities while mitigating privacy concerns becomes a challenging problem. Traditional methods rely on cryptographic tools and/or randomization to preserve privacy. Unfortunately, this alone may be inadequate for the emerging big data systems because they are mainly designed for traditional small-scale data sets. In this paper, we propose a novel framework to achieve privacy-preserving machine learning where the training data are distributed and each shared data portion is of large volume. Specifically, we utilize the data locality property of Apache Hadoop architecture and only a limited number of cryptographic operations at the Reduce() procedures to achieve privacy-preservation. We show that the proposed scheme is secure in the semi-honest model and use extensive simulations to demonstrate its scalability and correctness.

In cooperative cognitive radio networks (CCRNs), through cooperating with primary transmissions, secondary users (SUs) could access the spectrum resource when primary users (PUs) are transmitting. The existing schemes in CCRNs allocate the spectrum resource only to the cooperative relay SU. However, this may lead to the waste of spectrum resource, especially when the relay SU has light traffic load or poor channel condition. To better utilize the spectrum among all SUs in a secondary network, we design a spectrum resource utilization maximization scheme with joint consideration of relay selection and spectrum scheduling problems. With the goal to maximize the throughput of the secondary network, our scheme allocates spectrum among all SUs according to the diversity of secondary traffic load and the channel conditions. Besides, considering that the SUs are always energy limited, we also formulate the energy constraint for each SU to avoid the energy consumption exceeding the total available energy. Moreover, we study the resource allocation problem from long-term view under dynamic network setting, and design an online algorithm to solve it. Through extensive simulations, we show that the proposed scheme outperforms the existing schemes in terms of secondary network throughput.

The Internet of Things (IoT) is emerging as one of the major trends for the next evolution of the Internet, where billions of physical objects or things (including but not limited to humans) will be connected over the Internet, and a vast amount of information data will be shared among them. However, the current Internet was built on a host-centric communication model, which was primarily designed for meeting the demand of pair-wise peer-to-peer communications and cannot well accommodate various advanced data-centric services boosted by the IoT in which users care about content and are oblivious to locations where the content is stored. In this paper, we propose a novel architecture for the future Internet based on information-centric networking (ICN), which is called DataClouds, to better accommodate data-centric services. Different from existing ICN-based architectures, we take the sharing nature of data-centric services under the IoT into consideration and introduce logically and physically formed communities as the basic building blocks to construct the network so that data could be more efficiently shared and disseminated among interested users. We also elaborate on several fundamental design challenges for the Internet under this new architecture and show that DataClouds could offer more efficient and flexible solutions than traditional ICN-based architectures.

Cooperative cognitive radio networks (CCRNs) enable secondary users (SUs) to access primary resource by cooperation with active primary users (PUs). For the cooperation-generated resource, existing schemes in CCRNs allocate the resource only to the relay SUs. However, this may lead to inefficient spectrum utilization, when the relay SUs have poor channel condition or little traffic load for their own secondary transmissions. In this paper, considering user diversity in secondary networks, we focus on network-level throughput optimization for secondary networks, by allowing all SUs to optimally share the cooperation-generated period. Besides, considering the energy constraint on SUs, we formulate the resource allocation problem from long-term perspective, to reflect the time-varying change of user diversity in channel condition, traffic load and energy amount. We present an online SUM scheme to solve the long-term optimization problem. Although a mixed-integer and non-convex problem is involved in the SUM scheme, we transform the problem into multiple convex subproblems, and then optimally solve it with low computational complexity. Extensive simulations show that the proposed SUM scheme significantly outperforms the existing schemes.

Content dissemination is very useful for many mobile applications, like instant messaging, file sharing, and advertisement broadcast, etc. In real life, for various kinds of time-insensitive contents, such as family photos and video clips, the process of content dissemination forms a delay tolerant networks (DTNs). To improve the data forwarding performance in DTNs, several social-based approaches have been proposed, most of which leverage mobile users' social information, including contact history, moving trajectory, and personal profiles as metrics to design routing schemes. However, although the social-based approaches provide better performance, the revealing of mobile users' information apparently compromises their privacy. Moreover, users' contents may only be shared with a particular group of users rather everyone in the system. In this paper, we propose the PSaD: a Privacy-preserving Social-assisted content Dissemination scheme in DTNs. We apply users' verifiable attributes to establish their social relationships in terms of identical attributes in a privacy-preserving way. Besides, to provide the confidentiality of contents, our approach enables users to encrypt contents before the dissemination process, and only allows users who have particular attributes to decrypt them. By trace-driven simulations and experiments, we show the performance, privacy preservation, and efficiency of our proposed scheme.

Sensor-enabled radio frequency identification (RFID) technology has generated a lot of interest from industries lately. Integrated with miniaturized sensors, RFID tags can provide not only the IDs, but also valuable real-time information about the state of the objects or their surrounding environment, which can benefit many practical applications, such as warehouse management and inventory control. In this paper, we study the problem of designing efficient protocols for a reader to collect sensor-produced information from unknown target tags in an RFID system with minimum execution time. Different from information collection with all target tags known a priori, in the scenarios we consider, the reader has to first find out the target tags in order to read information from them, which makes traditional information collection protocols not efficient any more. We design a Bloom-filter-based information collection protocol (BIC) to address this challenging problem. A Bloom filter is constructed for the reader to efficiently determine the target tags, which significantly reduces the communication and time overhead. We also introduce the allocation vectors to coordinate the transmissions from different tags and minimize collision during information collection. Extensive simulation results demonstrate that our protocol is highly efficient in terms of execution time, and it performs much better than other solutions.

Mobile content dissemination is very useful for many mobile applications in delay tolerant networks (DTNs), like instant messaging, file sharing, and advertisement dissemination, etc. Recently, social-based approaches, which attempt to exploit social behaviors of DTN users to forward time-insensitive data, such as family photos and friends' sightseeing video clips, have attracted intensive attentions in designing routing schemes in DTNs. Most social-based schemes leverage users' contact history and social information (e.g., community and friendship) as metrics to improve the dissemination performance. In these schemes, users need to obtain others' social information to determine their dissemination strategy, which apparently compromises others users' privacy. Moreover, the owner of mobile contents may only want to disclose his/her data to a particular group of users rather than revealing it to the public. In this paper, we propose a privacy-preserving social-assisted mobile content dissemination scheme in DTNs. We apply users' verifiable attributes to establish their potential social relationships in terms of identical attributes in a privacy-preserving way. Besides, to provide the confidentiality of mobile contents, our approach enables users to encrypt contents before the dissemination process, and only allows users who have particular attributes to decrypt them. By trace-driven simulations and experiments, we show the security and efficiency of our proposed scheme.

Cognitive radio technology enables secondary users (SUs) to opportunistically use the vacant licensed spectrum and significantly improves the utilization of spectrum resource. Traditional architectures for cognitive radio networks (CRNs), such as cognitive cellular networks and cognitive ad hoc networks, impose energy-consuming cognitive radios to SUs' devices for communication and cannot efficiently utilize the spectrum harvested from the primary users (PUs). To enhance the spectrum and energy efficiencies of CRNs, we have designed a new architecture, which is called the Cognitive Capacity Harvesting network (CCH). In CCH, a collection of relay stations (RSs) with cognitive capability are deployed to facilitate the accessing of SUs. In this way, the architecture not only removes the requirement of cognitive radios from SUs and reduces their energy consumption, but also increases frequency reuse and enhances spectrum efficiency. In view of the importance of the RSs on the improvement of spectrum and energy efficiencies, in this paper, we study the RS placement strategy in CCH. A cost minimization problem is mathematically formulated under the spectrum and energy efficiency constraints. Considering the NP-hardness of the problem, we design a framework of heuristic algorithms to compute the near-optimal solutions. Extensive simulations show that the proposed algorithms outperform the random placement strategy and the number of required RSs obtained by our algorithms is always within 2 times of that in the optimal solution.

Cognitive radio (CR) technology opens the licensed spectrum bands for opportunistic usage and initiates spectrum trading to improve the spectrum utilization. In this paper, we investigate the path selection problem in multihop cognitive radio networks (CRNs) under constraints on flow routing, link scheduling and CR source's budget. We extend the per-user-based spectrum trading in prior work to CR session-based spectrum trading, and effectively develop the spectrum trading mechanisms based on the cross-layer optimization in multihop CRNs. We introduce a new service provider, called secondary service provider (SSP), to help CR sessions to select the paths for packet delivery. Considering the price of bands and the potential returning of primary services at different CR links, the SSP purchases the licensed spectrum and jointly conducts flow routing and link scheduling under the budget constraints. We also propose a 4D conflict graph to characterize the conflict relationship among CR links and mathematically formulate the path selection problem under multiple constraints into an optimization problem with the objective of maximizing the end-to-end throughput. Due to the NP-hardness of the problem, we have also developed a heuristic algorithm to find the approximate solution.

By enabling opportunistic secondary users' (SUs) usage of licensed spectrum, cognitive radio (CR) technology notably improves spectrum utilization. However, the fundamental multicast throughput optimization problem in cognitive radio networks (CRNs) is still under-explored. Considering spectrum availability and sharing fairness, in this paper, we propose a cross-layer approach to maximize the multicast throughput in multi-hop CRNs. We introduce a new service provider, called secondary service provider (SSP), to harvest the available spectrum and allocate the collected bands among SUs. The SSP also guides the transmissions of multicast CR sessions w.r.t. their contention and spectrum sharing fairness. Leveraging the proposed palmier structure for the multicast session and the multi-radio multi-band multicast (M3) conflict graph, we mathematically characterize the multicast flow routing and link scheduling, respectively. Based on the proportional fairness model, we formulate the multicast maximization problem under multiple cross-layer constraints in CRNs, and provide near-optimal solutions. Through simulations, we show that the performance of the proposed scheme is much better than that of schemes without cross-layer consideration.

Reliable broadcast is an important communication primitive for wireless mesh networks, which has many applications such as multimedia services and software upgrade. Recently, network coding is introduced into reliable broadcast to enhance the throughput of data transmissions. Existing network coding based reliable broadcast schemes, such as Pacifier and R-Code, fail to take advantage of the unique characteristic of reliable broadcast or the broadcast nature of wireless transmissions, which leads to redundant transmissions and performance degradation. In this paper, we propose a greedy strategy for network coding based reliable broadcast, which is called GreedyCode. GreedyCode opportunistically selects the forwarders with the highest transmission efficiency to transmit the encoded packets while the neighbors just listen. In order to measure the efficiency of broadcast transmission of a node, we also define a metric named One-hop Broadcast Throughput (OBT), which considers not only the current reception status of the destinations but also the quality of the broadcast link. Because GreedyCode only needs the information of its one-hop neighbors, so it can be distributed realized. The simulation results show that GreedyCode achieves 100% packet delivery ratio (PDR) and significantly reduces the number of transmissions and the broadcast delay.

Network coding is widely recognized as a promising approach to increase the throughput of wireless networks. In order to maximize the benefit of network coding, the consideration of potential coding opportunities is incorporated into the route selection, which is referred to as coding-aware routing. Most of existing coding-aware routing metrics are designed based on traditional routing metrics like expected transmission count (ETX) and fail to take many critical factors into account, such as traffic load, link transmission rate and interference. Therefore, the routes discovered with them are always sub-optimal. In this paper, we present a novel routing metric called Coding-aware Path Transmission Time (CPTT). CPTT considers the effect of traffic load, multirate, intra-flow and inter-flow interference as well as network coding and quantifies them in a unified manner, which can be used to accurately evaluate path performance and discover the path with high throughput. Through extensive simulations, we compare CPTT with different coding-aware routing metrics proposed in the literature and show that the paths selected with CPTT have maximum end-to-end throughput under network coding.

Privacy protection is of critical concern to Location-Based Service (LBS) users in mobile networks. Long-term pseudonyms, although appear to be anonymous, in fact empower third-party service providers to continuously track users' movements. Researchers have proposed the mix zone model to allow pseudonym changes in protected areas. In this paper, we investigate a new form of privacy attack to the LBS system that an adversary reveals a user's true identity and complete moving trajectory with the aid of side information. We propose a new metric to quantify the system's resilience to such attacks, and suggest using multiple mix zones to tackle this problem. A mathematical model is presented that treats the deployment of multiple mix zones as a cost constrained optimization problem. Furthermore, the influence of traffic density is also taken into account to enhance the protection effectiveness. The placement optimization problem is NP-hard. We therefore design two heuristic algorithms as practical and effective means to strategically select mix zone locations, and consequently reduce the privacy risks of mobile users trajectories. The effectiveness of our proposed solutions is demonstrated through extensive simulations on real-world mobile user data traces.

Sensor-enabled RFID technology has generated a lot of interest from industries lately. Integrated with miniaturized sensors, RFID tags could provide not only the IDs but also valuable real-time information about the state of the corresponding objects or the surrounding environment, which is beneficial to many practical applications, such as warehouse management and inventory control. In this paper, we study the problem on how to design efficient protocols to collect such sensor information from numerous tags in a large-scale RFID system with a number of readers deployed. Different from information collection in the small RFID system covered by only one reader, in the multi-reader scenario, each reader has to first find out which tags located in its interrogation region in order to read information from them. We start with two categories of warm-up solutions that are directly extended from the existing information collection protocols for single-reader RFID systems, and show that all of them do not work well for the multi-reader information collection problem due to their inefficiency of identifying the interrogated tags. Then, we propose a novel solution, called the Bloom filter based Information Collection protocol (BIC). In BIC, the interrogated tag identification can be efficiently achieved with a distributively constructed Bloom filter, which significantly reduces the communication overhead and thus the protocol execution time. Extensive simulations show that BIC performs better than all the warm-up solutions and its execution time is within 3 times of the lower bound.

In a cognitive radio network, primary service providers (PSPs) set prices for the vacant licensed bands and sell/lease them for pecuniary gains while the secondary service provider (SSP) can buy/rent the bands and support opportunistic spectrum accessing (OSA). However, due to the unpredictable activities of primary services, the SSP may suffer the monetary risk or failure to meet the traffic demands from the secondary users (SUs). It is challenging for the SSP to measure the risk for OSA, to choose the bands to use, and to split the traffic on the band-mix, when there are multiple vacant bands from PSPs. In this paper, we first introduce the X loss, an intuitive measurement for the risk for OSA. Although the X loss is attractively simple, it underestimates the potential risk for OSA and is mathematically not subadditive, which makes it difficult to support the band-mix selection for traffic splitting. To overcome this problem, we propose a more suitable risk measurement, which is subadditive and consistent with the SSP's perception of the risk. Based on the proposed risk metric, we formulate the band-mix selection problem as an optimization problem and solve it by linear programming.

The multi-hop cellular network (MCN) is an evolved paradigm for mobile communications, which integrates the ad hoc characteristics into the conventional cellular systems. Similar to ad hoc networks, the performance of MCNs relies on the hypothesis that each node accepts to forward traffic for the benefit of others, which may not hold with the possible presence of selfish users. In order to stimulate the collaboration among mobile nodes in MCNs, in this paper, we propose a light-weighted secure incentive protocol (LIP). We introduce a novel reward model, in which not the source and/or the destination but the network operator credits the forwarding nodes. It is shown that our model is much more realistic for MCNs in practice and simplifies the payment scheme design as well. LIP exploits a reactive receipt-submission mechanism to identify node behavior, which significantly reduces the communication overhead. Security analysis shows that LIP can resist various attacks. The efficiency of LIP is validated through the performance evaluation.

Cognitive Radio technology releases the spectrum from shackles of authorized licenses and facilitates the trading of spectrum bands. In the spectrum market, primary service providers (PSPs) set prices for the vacant licensed bands of primary users (PUs) and sell/lease them for pecuniary gains, and the secondary service provider (SSP) can buy/rent the bands and support the secondary users (SUs) for their opportunistic spectrum accessing (OSA) when primary services are not active. However, due to the unpredictable activities of primary services, the SSP may suffer the monetary risk or failure to satisfy the traffic demands from the SUs. It is challenging for the SSP to measure the risk for OSA, to choose the bands to access, and to split the overall traffic on the band-mix, when there are multiple vacant bands and uncertain spectrum supply from PSPs. To address the concerns of the SSP, in this paper, we first introduce the X loss, an intuitive measurement to evaluate the risk for OSA at a given confidence level. Although the X loss is attractively simple, it theoretically underestimate the potential risk for OSA. Meanwhile, the X loss requires strong assumptions to support band-mix selection for traffic splitting, i.e., the primary services of different bands must satisfy normal distribution, which is not necessarily true in practice. To overcome the weakness of the X loss, we further propose a more suitable risk measurement, the expected X loss, which is theoretically subadditive and practically consistent with the SSP's perception of risk for OSA. Based on the proposed measurement, we formulate the band-mix selection problem for traffic splitting into an optimization problem and solve it by linear programming.

Cognitive Radio technology releases the spectrum from shackles of authorized licenses and facilitates the trading of spectrum bands. In the spectrum market, primary users (PUs) set prices for their vacant bands and sell them for monetary gains, and secondary users (SUs) buy the bands and opportunistically use them to satisfy their service demands when the PUs are not active. However, when there are multiple bands available for the SUs to access, the SU confronts the challenges of how to choose bands and how to split his traffic over them considering both the contention from peer SUs as well as the unpredictable activities of the PUs. In this paper, we propose a return and risk model to represent these concerns of the SU, and help the SU to make appropriate decisions of traffic distribution over available spectrum bands, either the bands belonging to SU itself or the bands shared with PUs. The simulation and analysis show that our spectrum sharing scheme is efficient in terms of maximum return for given risk or minimum risk for given return, and is also effective in improving the spectrum utilization and SUs' satisfactory degrees.

This paper proposes C&M, a novel network coding scheme to deal with both inter-flow and intra-flow traffic in wireless networks. Prior work on wireless network coding design belongs to either inter-flow network coding or intra-flow network coding. C&M attempts to combine advantages of both network coding approaches to develop a more efficient network coding scheme. Based on COPE, C&M allows each node to make use of intra-flow network coding such as MORE to improve the transmission reliability in a lossy environment, consequently obtaining higher throughput. Moreover, we propose the multiple-path transmitting scheme to increase the throughput of wireless networks with low link delivery probability further. Finally, we provide our view on how to integrate network coding into network stack and discuss some potential benefits of C&M.